Abstract
Quantum computers utilize the fundamentals of quantum mechanics to solve computational problems more efficiently than traditional computers. Gate-model quantum computers are fundamental to implement near-term quantum computer architectures and quantum devices. Here, a quantum algorithm is defined for the circuit depth reduction of gate-model quantum computers. The proposed solution evaluates the reduced time complexity equivalent of a reference quantum circuit. We prove the complexity of the quantum algorithm and the achievable reduction in circuit depth. The method provides a tractable solution to reduce the time complexity and physical layer costs of quantum computers.
Similar content being viewed by others
Introduction
Gate-model quantum computers are realized by unitary operators (quantum gates) and quantum states1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29. As the technological limits of current semiconductor technologies will be reached within the next few years30,31,32,33,34,35,36,37,38,39,40, fundamentally different solutions provided by quantum technologies will be significant in the experimental realization of future computations15,16,17,18,31,32,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72. A quantum computer is set up with a quantum gate structure, that is, via a set of unitary operators. These quantum gates can realize different quantum operations and can be defined on different numbers of input quantum states15,16,17,18,41,42,43,45,52,53. In a quantum computer environment, the depth of the quantum gate structure refers to the number of time steps (time complexity) required for the quantum operations making up the circuit to run on the quantum hardware15,16,17,18,41,42,43,45,52,53,54,55,56,57,58,59. A crucial problem here is the time complexity reduction of the quantum gate structure of the quantum computer. Practically, this problem is such that an equivalent quantum state of the output quantum state of the original the reference quantum circuit (e.g., non-reduced time complexity circuit) can be obtained using a reduced time complexity quantum gate structure. Particularly, currently there exists no plausible and implementable solution for the time complexity reduction of quantum computers. Gate-model quantum computer implementations are affected by the problem of high time complexities and a universal (i.e., platform independent) and tractable solution for the time complexity reduction is essential. Relevant implication of this problem is the high economic cost of the physical apparatuses required for experimentally implementing practical quantum computation: specifically, the high economic cost of the high-precision quantum hardware elements required in the implementation of high-performance quantum circuits.
The quantum circuit of the quantum computer is modeled as an arbitrary quantum circuit with an arbitrary circuit depth formulated via a unitary sequence of L unitary operators. Each unitary is set via a particular Pauli operator and gate parameter (see also Section 2 for a detailed description). The input problem fed into the quantum computer is an arbitrary computational problem \({\mathscr{P}}\) with an objective function C. The C objective function is a subject of maximization via the quantum computer, i.e., via the unitaries of the circuit structure of the quantum computer. The objective function can model arbitrary combinatorial optimization problems9,10,42, large-scale programming problems10 such as the graph coloring problem, molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, or the vehicle routing problem10. For a detailed description of input problems, we suggest2,3,4,8,9,10,42,43,44,45.
Another important application of gate-model quantum computations is the near-term quantum devices of the quantum Internet20,30,36,37,38,39,46,47,48,49,59,61,62,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93.
Here, we define a quantum algorithm for the time complexity reduction of any quantum circuit of quantum computers set up with an arbitrary number of unitary gates. The aim of the proposed framework is to reduce the time complexity of an arbitrary reference quantum circuit and a maximization of the objective function of the computational problem fed into the quantum computer. The method defines the reduced time complexity equivalent of the reference quantum circuit and recovers the reference output quantum state via the reduced time complexity quantum circuit (Note: the terminology of quantum state refers to an input or output quantum system, while the terminology of quantum gate refers to a unitary operator.). The reduced structures are determined via a pre-processing phase in the logical layer, and only the reduced time complexity quantum circuit and reduced quantum state are implemented in the physical layer. The pre-rocessing phase integrates a machine learning94,95,96,97 unit for the parameter optimization. The high complexity reference quantum circuit and reference quantum input are characterized only in the pre-processing phase without any physical level implementation. The framework applies a quantum algorithm on the output of the reduced quantum gate structure to recover the equivalent quantum state of the output quantum state of the non-reduced reference structure. In particular, the proposed framework and the defined quantum algorithm are universal since they have no requirements for the structure of the reference (e.g., non-reduced) quantum circuit subject to be reduced, for the number of unitaries in the reference structure, for the size of the input quantum state of the reference quantum circuit, nor for the dimensions of the actual quantum state. The quantum algorithm is defined as a fixed, auxiliary hardware component for an arbitrary quantum computer environment, with a pre-determined constant computational complexity as an auxiliary cost of the application of the algorithm. Specifically, we prove that the auxiliary cost of the proposed quantum algorithm is orders lower than the reachable amount of the reduction in time complexity, and the computational cost of the quantum algorithm becomes negligible in practice. The method also allows significantly reducing the economic cost of physical layer implementations, since the required elements and high-cost hardware components can be reduced in an experimental setting.
The novel contributions of our manuscript are as follows:
- 1.
We define a quantum algorithm for circuit depth reduction of quantum circuits of gate-model quantum computers.
- 2.
We define the computational cost of the proposed quantum algorithm and prove that it is significantly lower than the gainable reduction in time complexity.
- 3.
The algorithm provides a tractable solution to reduce circuit depth and the economic cost of implementing the physical layer quantum computer by reducing quantum hardware elements.
- 4.
The results are useful for experimental gate-model quantum computations and near-term quantum devices of the quantum Internet.
This paper is organized as follows. Section 2 defines the system model. Section 3 proposes the quantum algorithm and proves the computational complexity. Section 4 discusses the performance of the algorithm. Finally, Section 5 concludes the results. Supplemental material is included in the Appendix.
System Model
Let QG0 be a reference quantum circuit (quantum gate structure) with a sequence of L unitaries42, defined as
where \(\vec{\theta }\) is the L-dimensional vector of the gate parameters of the unitaries (gate parameter vector),
and an i-th unitary gate Ui(θi) is evaluated as
where Pi is a generalized Pauli operator acting on a few quantum states (qubits in an experimental setting) formulated by the tensor product of Pauli operators {σx, σy, σz}42. Note, that \(U(\vec{\theta })\) in (1) identifies a unitary resulted from the serial application of the L unitary operators UL(θL)UL−1(θL−1) … U1(θ1), and for an input quantum state |φ〉,
A qubit system example with a sequence of L unitaries is as follows. Let assume that the QG0 structure of the quantum computer consists of g single-qubit and m two-qubit unitaries, L = g + m, such that a j-th single-qubit gate realizes an \({X}_{j}={\sigma }_{x}^{j}\) operator, while a two-qubit gate realizes a \({Z}_{j}{Z}_{k}={\sigma }_{z}^{j}{\sigma }_{z}^{k}\) operator (see also42). Then, at a particular objective function C of an arbitrary computational problem subject of a maximization via the quantum computer, the \(U(\vec{\theta })\) sequence from (1) can be rewritten as
where
where \(\vec{\beta }\) is the gate parameter vector of the g single-qubit unitaries,
while B is defined as
with
and
where Bj = Xj, while the two-qubit unitaries are defined as
where 〈jk〉 ∈ QG0 is a physical connection between qubits j and k in the hardware-level of the QG0 structure of the quantum computer, \(\vec{\gamma }\) is the gate parameter vector of the m two-qubit unitaries
while
where Cjk is a component of the objective function, while unitary U(Cjk, γjk) for a given 〈jk〉 is defined as
where
At a particular physical connectivity of QG0, the objective function C therefore can be written as
where Cjk(z) is the objective function component evaluated for a given 〈jk〉, as
while z is an N-length input bitstring,
where zi identifies an i-th bit, zi ∈ {−1, 1}.
For a given z, a |z〉 computational basis state is defined as
and the |ϕ〉 output system of QG0 is as
that can be evaluated further via (6) and (11), as
To achieve the quantum parallelism, the input system |φ〉 = |X〉 of the quantum computer is set as an N-length d-dimensional (d = 2 for a qubit system) quantum state in the superposition of all possible dN states. For a qubit system, it means that input |X〉 is an N-qubit quantum state in a superposition of all possible 2N states |0〉 to |2N − 1〉, and the computations are performed on 2N states in parallel in the quantum computer.
Let |X〉 be a superposed input system of the non-reduced QG0 gate structure:
where |xi〉 is an i-th input state (represented as an N-length bit string for a qubit system), i = 1, …, n, n = dN, of the QG0 structure of the quantum computer.
To describe the parallel processing of the n input vectors of |X〉 (see (22)), {|x1〉, …, |xn〉} of |X〉 (see (22)) in the quantum computer, let \({\vec{\theta }}_{i}\) be the gate parameter vector associated with a given |xi〉 of |X〉:
Let X be the classical representation of |X〉 in (22) to get
where xi is the classical representation of |xi〉. (Note, that X and xi are not accessible in the quantum computer, since the quantum algorithm operates in the quantum regime on quantum states. The classical representation is used only as an abstracted auxiliary representation to describe the steps of the algorithm in a plausible manner).
Then, let \({U}_{0}(\vec{\theta })\) be the non-reduced gate structure matrix of QG0:
where
and \({U}_{0}({\vec{\theta }}_{i})\) is the unitary sequence associated with |xi〉 in QG0, defined as
At an n-dimensional output vector
and the |Y〉 output quantum state of the non-reduced QG0 structure is
To define the reduced gate structure, QG*, it is necessary to find a reduced \(U({\vec{\theta }}_{i}^{{\prime} })\) with a reduced input \(|{\tilde{x}}_{i}\rangle \), for all i.
Then, let \(\tilde{X}\) be the classical representation of the reduced quantum state \(|\tilde{X}\rangle \) fed into QG*, as
and
where N* is the number of d-dimensional (physical) quantum states that formulate \(|\tilde{X}\rangle \), n* = dN*, while the unitaries \(U(\vec{\theta }{\prime} )\) of QG* are
where
and \(U({\vec{\theta }}_{i}^{{\prime} })\) is the reduced unitary sequence associated with \(|{\tilde{x}}_{i}\rangle \), defined as
The pre-processing phase determines output Z of QG* as a classical representation
and the output quantum state |Z〉 of QG* therefore yields
The notations of the system model are also summarized in Table A.1 of the Supplemental Information.
Problem statement
Problems 1–3 summarize the problems to be solved.
Problem 1
Find a classical pre-processing \({\mathscr{P}}\) for calculating the \(|\tilde{X}\rangle \) reduced input system and the gate parameters of the QG* reduced time complexity gate structure.
Problem 2
Find a universal (independent of the number L of the unitaries in QG0) unitary operator UR with a set \({\mathscr{R}}\) of quantum registers to recover output |Y〉 of the non-reduced QG0 structure from output |Z〉 of the QG* reduced time complexity gate structure.
Problem 3
Determine the time complexity of UR and the reduction in the overall time complexity of QG*.
Theorems 1–3 give the resolutions of Problems 1–3.
The non-reduced time complexity quantum circuit QG0 (reference circuit) with an input quantum state |X〉 is showed in Fig. 1(a). Figure 1(b) depicts the system model for the problem resolution. The method is realized via unitary UR and \({\mathscr{P}}\) pre-processing, such that UR is implemented in the physical layer, while \({\mathscr{P}}\) is only a logical-layer process. Only the reduced input quantum state \(|\tilde{X}\rangle \) and the reduced quantum gate structure QG* must be built up in the physical layer to yield the reference output system |Y〉 of the reference circuit QG0 via |YR〉. In both cases, the output states are measured via a measurement M to get a classical bitring for the objective function evaluation. As a next step, the gate parameter values of the unitaries of the circuits are calibrated until an optimal objective function value is not reached. The calibration of the gate parameters is a separate optimization procedure and its aim is fundamentally differ from the aim of \({\mathscr{P}}\), and therefore it is not part of the circuit depth reduction method. Note that existing algorithms can be utilized for this task (such as a the algorithms proposed in19 and20, or some gradient independent methods98).
Pre-processing
Theorem 1
There exists a \({\mathscr{P}}\) pre-processing to determine the \(|\tilde{X}\rangle \) input system and the \(U({\vec{\theta }}_{i}^{{\prime} })\) gate parameters, i = , …, n, for the reduced QG* gate structure for an arbitrary non-reduced QG0 structure with \(U({\vec{\theta }}_{i})\) and input |X〉.
Proof. The \({\mathscr{P}}\) pre-processing phase can be decomposed as \({\mathscr{P}}={\mathscr{C}}{\mathscr{L}}\), where \({\mathscr{C}}\) is a computational block, while \({\mathscr{L}}\) is a machine learning control block to calibrate the results of \({\mathscr{C}}\). We first define block \({\mathscr{C}}\), then discuss \({\mathscr{L}}\). The \({\mathscr{P}}\) pre-processing is a procedure to stabilize the output of the reduced quantum circuit. \({\mathscr{P}}\) is defined between the components \({\mathscr{C}}\) and \({\mathscr{L}}\) to evaluate \(|\tilde{X}\rangle \) and to set the gate parameters of the reduced quantum circuit structure QG* using the reference output |Y〉 of QG0. Note, as the output |YR〉 is fed into an M measurement block, the measurement results provide a feedback to calibrate \({\mathscr{P}}\) in every subroutine of the protocol to produce a final saturated output. The Δ output of the \({\mathscr{L}}\) machine learning control unit is used as a feedback in unit \({\mathscr{C}}\). For the definition of Δ, see (116) in Algorithm 1.
In the \({\mathscr{C}}\) computational block, the reduced \(U({\vec{\theta }}_{i}^{{\prime} })\) and \({\tilde{x}}_{i}\) are determined for ∀i, in the following manner. Note, since \({\mathscr{P}}\) outputs the parameters of the reduced quantum gate structure, the extra complexity of a quantum structure can be replaced with classical complexity in the form of machine learning in the proposed framework.
Operation \({\mathscr{C}}\) sets a one-dimensional discrete cosine transform99 in the reduction method, thus a matrix G is defined as a generator matrix to evaluate the output coefficients of \({\mathscr{C}}\), see later (45). The definition of \({\mathscr{C}}\) (see later in (40)) comes from the fact that any U unitary operator can be rewritten via the cos and sin functions, and using cosine functions rather than sine functions is critical for a compression99. In our setting, this is because fewer cosine functions are needed to approximate a particular U unitary operator.
Let xi be the classical representation of |xi〉, and \({y}_{i}=U({\vec{\theta }}_{i}){x}_{i}\) be the classical representation of |yi〉. Using the sequences of the L unitaries in (29), define a matrix G with n coefficients ai, i = 1, …, n, as
where
where θi,j identifies the gate parameter of a j-th unitary Ui,j(θ) associated to an i-th input xi, while unitary sequence \({U}_{0}({\vec{\theta }}_{i})\) to an i-th input xi is
where Pi is a generalized Pauli operator.
First, the \({\mathscr{C}}\) operation (one-dimensional discrete cosine transform99) is applied to the input matrix G from (37),
where cp is the p-th coefficient of \({\mathscr{C}}\),
where
and Ap is
The coefficients of \({\mathscr{C}}\) defines matrix γ as
where · is the inner product,
where coefficients ak-s are given in (37), and χ is
where ςi is an n-length vector
Then, the n-length output vector κ of \({\mathscr{C}}\) is defined as
where Y is given in (28), while Ωi is as
Then, using the coefficients (41), (42) and (43) of \({\mathscr{C}}\), \(|{\tilde{x}}_{p}\rangle \) of the reduced state \(|\tilde{X}\rangle \) from (31) can be evaluated via the \({\tilde{x}}_{p}\) components of \(\tilde{X}\) of (30). A p-th input \(|{\tilde{x}}_{p}\rangle \) for QG* is defined via (49) as
and the reduced quantum gate sequence \(U({\vec{\theta }}_{p}^{{\prime} })\) of \(|{\tilde{x}}_{p}\rangle \) in QG*, as
where Pp is a generalized Pauli operator, and \(\Sigma {\tilde{\theta }}_{p}\) is as
Therefore, the quantum state |Z〉 of QG* is
The description of the \({\mathscr{L}}\) machine learning control unit is as follows. Unit \({\mathscr{L}}\) uses the results of \({\mathscr{C}}\) to provide feedback for the pre-processing via supervised machine learning control.
The \({\mathscr{L}}\) machine learning algorithm for the pre-processing control is defined in Algorithm 1.
The steps of the \({\mathscr{P}}\) pre-processing method is given in Procedure 1.
■
Quantum Algorithm
Theorem 2
The |Y〉 output of the non-reduced QG0 structure can be recovered from the output |Z〉 of the reduced structure QG* via a unitary operator UR.
Proof. Let \(|\tilde{X}\rangle \) be the input quantum state fed into the reduced structure QG*, and let |Z〉 (see (53)) be the output of the reduced gate structure. The task here is therefore to recover \(|Y\rangle ={U}_{0}(\vec{\theta })|X\rangle \) from |Z〉. The problem is solved via a unitary UR, as follows.
Without loss of generality, in an i-th step, i = 1, …, n, the goal of the UR operation is to calculate the quantum state as
where κ is as in (48), while ωi = (ωi,1, …, ωi,n)T is an n-length vector defined for a given j, as
where ∑θi is as given in
where \(\Sigma {\tilde{\theta }}_{p}\) is given in (52).
Then let
such that
Applying UR for all i, yields the recovered quantum state |YR〉 as
where an i-th |xi〉 is as
where i ≤ n − 1, and p ≥ 0, and \({\tilde{x}}_{p}\) is as given in (50); while the \({U}_{0}({\vec{\theta }}_{i})\) gate parameters (see (39)) of the L unitaries for a given i are evaluated as
The unitary UR is defined via a set \({\mathscr{R}}\) of quantum registers as
where |Ri〉 is the i-th quantum register. The registers are initialized via set \({{\mathscr{R}}}_{0}\) as
where κ is given in (48), while ∂ and η are initial parameters defined as
and
where
where \({\Omega }_{i}=U({\vec{\theta }}_{i}^{{\prime} }){\tilde{x}}_{i}\), and
where \({y}_{i}=U({\vec{\theta }}_{i}){x}_{i}\).
Then, unitary UR is defined as
where
and US is a unitary defined as
with eigenstate
where U0 is an initial unitary operator that prepares state |R5〉 = |ωi〉 for a given index state |R4〉 = |i〉, where ωi is given in (55); from an initial |R4〉|R5〉 = |i〉|0〉 as
in the register set \({{\mathscr{R}}}_{0}\) (see (63)), where \(\oplus \) is the CNOT operation, while \({O}_{{\Phi }_{i}}\) is an oracle applied on \({{\mathscr{R}}}_{0}\) to compute Φi (54), defined as
where \({\mathscr{R}}{\prime} \) is the resulting register set, while \({O}_{{f}_{i}}\) is an oracle that outputs function fi, as
Specifically, note that (70) changes only the phase of the state as \({(-1)}^{{f}_{i}}\), where fi is given in (74), while
Applying (74) on (63) yields a register state \({O}_{{f}_{i}}({{\mathscr{R}}}_{0})\) as
where \({(-1)}^{{f}_{i}}\) is the eigenvalue of US in (70).
Then, using the register set (63), let |ϕ0〉 be the input state for UR as
Applying (68) k-times on (77) yields
The k iteration number in (78) is a random number, k < c, where \(c=\,{\rm{\min }}\{1.2\cdot m,\sqrt{n}\}\), and m is initialized as m = 199.
Then let OZ be an oracle defined on \({\mathscr{R}}{\prime} \) as
Applying OZU0 on (78), outputs system state
In particular, in system state (80), the state of register |R6〉 is
therefore yields (59), such that
holds for all i of |YR〉, due to the conditions set in the pre-processing procedure \({\mathscr{P}}\) (see (67)).
Assuming that the input system (77) for UR is prepared for R-times and the output register (81) is measured for R-times, i.e., UR is applied for R times in overall, in an r-th repetition, r = 1, …, R, the parameters of the procedure can be valuated as
where
where \({\Phi }_{i}^{(r)}\) is the measured value of |Φi〉 in the r-th repetition of UR, while q(r) is the number of coefficients have been already found99.
The actual value of r requires no increment if the relation
holds, where τ(r) is a threshold value in the r-th iteration. Otherwise, the value of r can be increased, r = r + 1, as r < R.
The steps of the quantum algorithm UR are given in Algorithm 2.
Distortion measure
As (81) is prepared in Step 4 of UR, the state |YR〉 can be measured to get the classical string zR to evaluate objective function C(zR), as follows. Measure register |R6〉 of \({\mathscr{R}}\) via a measurement operator M to evaluate objective function C(zR), where zR is a classical string resulted from the measurement of |YR〉, while C is an objective function of an arbitrary computational problem fed into the quantum computer.
The \({\mathscr{D}}\) distortion coefficient associated with the |YR〉 recovered quantum state (59) can be evaluated at a particular objective function C, associated to the computational problem fed into the quantum computer as
where z is a classical string resulting from the M measurement of |Y〉, while zR is a classical string resulting from the M measurement of |YR〉.
Precisely, assuming R measurement rounds, an average of distortion yields
where C(r)(z) and C(r)(zR) are the objective function values respectively associated with z and zR in the r-th round, r = 1, …, R.
Computational Complexity
Theorem 3
Quantum algorithm UR can be implemented with time complexity \({\mathscr{O}}(\sqrt{n})\) for the time complexity reduction of any non-reduced QG0 with an arbitrary number of L unitaries.
Proof. Let
be a global space spanned by |i〉, an n-dimensional vector |b〉, and by |c〉, which represents the inner product state.
Particularly, the UR unitary in (68) applied on an input |φ〉 formulated via set \({\mathscr{R}}\) of quantum registers gives
where
thus UR can be interpreted as a rotation on an n-dimensional subspace \({\mathscr{S}}\{|i\rangle \}\), 0 ≤ i < n, i.e., on a span of all |i〉.
Let ∏ be the solution set with conditions (82) for all i of ∏,
and let \(|\varUpsilon \rangle \in {\mathscr{S}}\{|i\rangle \}\) be the superposition of all solutions:
The operation UR on |φ〉 yields the state \(|{\phi }^{\ast }\rangle \in {\mathscr{S}}\{|i\rangle \}\) (see (80)):
thus, UR is a rotation on the subspace \({\mathscr{S}}\{|{\phi }^{\ast }\rangle ,|\varUpsilon \rangle \}\) by angle \({\theta }_{{U}_{R}}\) towards (92), as
where |∏| is the number of solutions (cardinality of solution set ∏).
UR can be implemented as a rotation of \({\theta }_{{U}_{R}}\) on subspace \({\mathscr{S}}\{|i\rangle \}\) (instead of a rotation on global space (88)) via a generalized quantum searching100 that yields time complexity \({\mathscr{O}}(\sqrt{n})\) for an arbitrarily large quantum circuit QG0. ■
Performance Evaluation
Assuming that the initial time complexity of the QG0 non-reduced gate structure is
where N is the number of d-dimensional (physical) quantum states in the superposed input system, and L is the number of unitaries in QG0, the time complexity of the reduced QG* structure is
where N* is the number of d-dimensional (physical) quantum states in the reduced superposed input system, and L* is the number of unitaries in the reduced gate structure QG*.
Since the complexity of the proposed scheme is
the result of (96) is a reduced time complexity with respect to (95), as the relation
holds; thus
The overall complexity of the QG* reduced structure at the application of UR is therefore
Figure 2 depicts the resulting time complexities for a qubit implementation (N-qubit superposed input system, and qubit gate structure with L unitaries).
To achieve time complexity reduction using \(|\tilde{X}\rangle \) and QG* instead of |X〉 and QG0, the relation \({\mathscr{O}}({N}^{\ast }{L}^{\ast }) < {\mathscr{O}}(\ell )={\mathscr{O}}(NL-\sqrt{n})\) must be straightforwardly satisfied, i.e., the initial complexity \({\mathscr{O}}(NL)\) has to be reduced by more than \({\mathscr{O}}(\sqrt{n})\). Since the complexity of the procedure is independent from the actual size of the gate structure, the cost remains constant \({\mathscr{O}}(\sqrt{n})\) for an arbitrarily large L.
Conclusions
Gate-model quantum computers are equipped with a collection of quantum states and unitary quantum gates. The realization of the quantum circuit of a quantum computer requires high fidelity, high precision, and high-level control. Since both the timecomplexity (depth of the circuits) and the economic costs of these implementations are high in practical scenarios, a reduction of these costs is essential. Here, we defined a quantum algorithm for reducing the circuit depth of gate-model quantum computers. The method achieves a reduction in the physical layer allowing significantly reducing implementation costs. The framework is flexible and can be used for arbitrary circuit depths.
Submission note
Parts of this work were presented in conference proceedings101.
Ethics statement
This work did not involve any active collection of human data.
References
Preskill, J. Quantum Computing in the NISQ era and beyond. Quantum 2, 79 (2018).
Arute, F. et al. Quantum supremacy using a programmable superconducting processor, Nature, Vol 574, https://doi.org/10.1038/s41586-019-1666-5 (2019).
Harrow, A. W. & Montanaro, A. Quantum Computational Supremacy. Nature 549, 203–209 (2017).
Aaronson, S. & Chen, L. Complexity-theoretic foundations of quantum supremacy experiments. Proceedings of the 32nd Computational Complexity Conference, CCC ’17, pages 22:1-22:67, (2017).
IBM. A new way of thinking: The IBM quantum experience, http://www.research.ibm.com/quantum (2017).
Alexeev, Y. et al. Quantum Computer Systems for Scientific Discovery, arXiv:1912.07577 (2019).
Loncar, M. et al. Development of Quantum InterConnects for Next-Generation Information Technologies, arXiv:1912.06642 (2019).
Foxen, B. et al. Demonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms, arXiv:2001.08343 (2020).
Ajagekar, A., Humble, T. & You, F. Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems. Computers and Chemical Engineering 132, 106630 (2020).
Ajagekar, A. & You, F. Quantum computing for energy systems optimization: Challenges and opportunities. Energy ume 179, 76–89 (2019).
Harrigan, M. et al. Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor, arXiv:2004.04197v1 (2020).
Rubin, N. et al. Hartree-Fock on a superconducting qubit quantum computer, arXiv:2004.04174v1 (2020).
Lloyd, S. Quantum approximate optimization is computationally universal, arXiv:1812.11075 (2018).
Shor, P. W. Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science (1994).
Debnath, S. et al. Demonstration of a small programmable quantum computer with atomic qubits. Nature 536, 63–66 (2016).
Barends, R. et al. Superconducting quantum circuits at the surface code threshold for fault tolerance. Nature 508, 500–503 (2014).
Ofek, N. et al. Extending the lifetime of a quantum bit with error correction in superconducting circuits. Nature 536, 441–445 (2016).
Kielpinski, D., Monroe, C. & Wineland, D. J. Architecture for a large-scale ion-trap quantum computer. Nature 417, 709–711 (2002).
Gyongyosi, L. Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers, Scientific Reports, https://doi.org/10.1038/s41598-020-61316-4 (2020).
Gyongyosi, L. & Imre, S. Training Optimization for Gate-Model Quantum Neural Networks, Scientific Reports, https://doi.org/10.1038/s41598-019-48892-w (2019).
Gyongyosi, L. & Imre, S. Dense Quantum Measurement Theory, Scientific Reports, https://doi.org/10.1038/s41598-019-43250-2 (2019).
Gyongyosi, L. & Imre, S. State Stabilization for Gate-Model Quantum Computers, Quantum Information Processing, https://doi.org/10.1007/s11128-019-2397-0, (2019).
Gyongyosi, L. & Imre, S. Quantum Circuit Design for Objective Function Maximization in Gate-Model Quantum Computers, Quantum Information Processing, https://doi.org/10.1007/s11128-019-2326-2 (2019).
Brandao, F. G. S. L., Broughton, M., Farhi, E., Gutmann, S. & Neven, H. For Fixed Control Parameters the Quantum Approximate Optimization Algorithm’s Objective Function Value Concentrates for Typical Instances, arXiv:1812.04170 (2018).
Zhou, L.,Wang, S.-T., Choi, S., Pichler, H. & Lukin, M. D. Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices, arXiv:1812.01041 (2018).
Lechner, W. Quantum Approximate Optimization with Parallelizable Gates, arXiv:1802.01157v2 (2018).
Crooks, G. E. Performance of the Quantum Approximate Optimization Algorithm on the Maximum Cut Problem, arXiv:1811.08419 (2018).
Ho, W. W., Jonay, C. & Hsieh, T. H. Ultrafast State Preparation via the Quantum Approximate Optimization Algorithm with Long Range Interactions, arXiv:1810.04817 (2018).
Song, C. et al. 10-Qubit Entanglement and Parallel Logic Operations with a Superconducting Circuit. Physical Review Letters 119(no. 18), 180511 (2017).
Laurenza, R. & Pirandola, S. General bounds for sender-receiver capacities in multipoint quantum communications. Phys. Rev. A 96, 032318 (2017).
Gyongyosi, L., Imre, S. & Nguyen, H. V. A Survey on Quantum Channel Capacities. IEEE Communications Surveys and Tutorials 99, 1, https://doi.org/10.1109/COMST.2017.2786748 (2018).
Imre, S. & Gyongyosi, L. Advanced Quantum Communications - An Engineering Approach. Wiley-IEEE Press (New Jersey, USA), (2012).
Shor, P. W. Scheme for reducing decoherence in quantum computer memory. Phys. Rev. A 52, R2493–R2496 (1995).
Petz, D. Quantum Information Theory and Quantum Statistics, Springer-Verlag, Heidelberg, (2008).
Bacsardi, L. On the Way to Quantum-Based Satellite Communication. IEEE Comm. Mag. 51(08), 50–55 (2013).
Gyongyosi, L. & Imre, S. Entanglement-Gradient Routing for Quantum Networks, Sci. Rep., Nature, https://doi.org/10.1038/s41598-017-14394-w (2017).
Gyongyosi, L. & Imre, S. Entanglement Availability Differentiation Service for the Quantum Internet, Sci. Rep., Nature, https://doi.org/10.1038/s41598-018-28801-3 (2018).
Gyongyosi, L. & Imre, S. Multilayer Optimization for the Quantum Internet, Sci. Rep., Nature, (2018).
Gyongyosi, L. & Imre, S. Decentralized Base-Graph Routing for the Quantum Internet, Phys. Rev. A, American Physical Society, (2018).
Gyongyosi, L. & Imre, S. A Survey on Quantum Computing Technology, Computer Science Review, Elsevier, https://doi.org/10.1016/j.cosrev.2018.11.002, ISSN: 1574-0137, (2018).
Farhi, E., Goldstone, J. & Gutmann, S. A Quantum Approximate Optimization Algorithm. arXiv:1411.4028. (2014).
Farhi, E., Goldstone, J., Gutmann, S. & Neven, H. Quantum Algorithms for Fixed Qubit Architectures. arXiv:1703.06199v1 (2017).
Farhi, E. & Neven, H. Classification with Quantum Neural Networks on Near Term Processors, arXiv:1802.06002v1 (2018).
Farhi, E., Goldstone, J., Gutmann, S. & Zhou, L. The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size, arXiv:1910.08187 (2019).
Farhi, E., Goldstone, J. & Gutmann, S. A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem. arXiv:1412.6062. (2014).
Pirandola, S. End-to-end capacities of a quantum communication network. Commun. Phys. 2, 51 (2019).
Pirandola, S., Laurenza, R., Ottaviani, C. & Banchi, L. Fundamental limits of repeaterless quantum communications. Nature Communications 8, 15043, https://doi.org/10.1038/ncomms15043 (2017).
Pirandola, S. et al. Theory of channel simulation and bounds for private communication. Quantum Sci. Technol. 3, 035009 (2018).
Van Meter, R. Quantum Networking, John Wiley and Sons Ltd, ISBN 1118648927, 9781118648926 (2014).
Biamonte, J. et al. Quantum Machine Learning. Nature 549, 195–202 (2017).
LeCun, Y., Bengio, Y. & Hinton, G. Deep Learning. Nature 521, 436–444 (2014).
Monz, T. et al. Realization of a scalable Shor algorithm. Science 351, 1068–1070 (2016).
Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning. MIT Press. Cambridge, MA, (2016).
Rebentrost, P., Mohseni, M. & Lloyd, S. Quantum Support Vector Machine for Big Data Classification. Phys. Rev. Lett. 113 (2014).
Lloyd, S. The Universe as Quantum Computer, A Computable Universe: Understanding and exploring Nature as computation, Zenil, H. ed., World Scientific, Singapore, 2012, arXiv:1312.4455v1 (2013).
Lloyd, S., Mohseni, M. & Rebentrost, P. Quantum algorithms for supervised and unsupervised machine learning, arXiv:1307.0411v2 (2013).
Lloyd, S., Garnerone, S. & Zanardi, P. Quantum algorithms for topological and geometric analysis of data. Nat. Commun., 7, arXiv:1408.3106 (2016).
Lloyd, S. et al. Infrastructure for the quantum Internet. ACM SIGCOMM Computer Communication Review 34, 9–20 (2004).
Lloyd, S., Mohseni, M. & Rebentrost, P. Quantum principal component analysis. Nature Physics 10, 631 (2014).
Schuld, M., Sinayskiy, I. & Petruccione, F. An introduction to quantum machine learning. Contemporary Physics 56, pp. 172–185. arXiv: 1409.3097 (2015).
Krisnanda, T. et al. Probing quantum features of photosynthetic organisms. npj Quantum Inf. 4, 60 (2018).
Krisnanda, T. et al. Revealing Nonclassicality of Inaccessible Objects. Phys. Rev. Lett. 119, 120402 (2017).
Krisnanda, T. et al. Observable quantum entanglement due to gravity. npj Quantum Inf. 6, 12 (2020).
Krisnanda, T. et al. Detecting nondecomposability of time evolution via extreme gain of correlations. Phys. Rev. A 98, 052321 (2018).
Shannon, K., Towe, E. & Tonguz, O. On the Use of Quantum Entanglement in Secure Communications: A Survey, arXiv:2003.07907, (2020).
Amoretti, M. & Carretta, S. Entanglement Verification in Quantum Networks with Tampered Nodes, IEEE Journal on Selected Areas in Communications, https://doi.org/10.1109/JSAC.2020.2967955 (2020).
Cao, Y. et al. Multi-Tenant Provisioning for Quantum Key Distribution Networks with Heuristics and Reinforcement Learning: A Comparative Study, IEEE Transactions on Network and Service Management, https://doi.org/10.1109/TNSM.2020.2964003 (2020).
Cao, Y. et al. Key as a Service (KaaS) over Quantum Key Distribution (QKD)-Integrated Optical Networks, IEEE Comm. Mag., https://doi.org/10.1109/MCOM.2019.1701375 (2019).
Farhi, E., Gamarnik, D. & Gutmann, S. The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: A Typical Case, arXiv:2004.09002v1 (2020).
Farhi, E., Gamarnik, D. & Gutmann, S. The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: Worst Case Examples, arXiv:2005.08747 (2020).
Brown, K. A. & Roser, T. Towards storage rings as quantum computers. Phys. Rev. Accel. Beams 23, 054701 (2020).
Sax, I. et al. Approximate Approximation on a Quantum Annealer, arXiv:2004.09267 (2020).
Pirandola, S. & Braunstein, S. L. Unite to build a quantum internet. Nature 532, 169–171 (2016).
Wehner, S., Elkouss, D. & Hanson, R. Quantum internet: A vision for the road ahead. Science 362, 6412 (2018).
Pirandola, S. Bounds for multi-end communication over quantum networks. Quantum Sci. Technol. 4, 045006 (2019).
Pirandola, S. Capacities of repeater-assisted quantum communications, arXiv:1601.00966 (2016).
Pirandola, S. et al. Advances in Quantum Cryptography, arXiv:1906.01645 (2019).
Cacciapuoti, A. S. et al. Quantum Internet: Networking Challenges in Distributed Quantum Computing, arXiv:1810.08421 (2018).
Caleffi, M. End-to-End Entanglement Rate: Toward a Quantum Route Metric, 2017 IEEE Globecom, https://doi.org/10.1109/GLOCOMW.2017.8269080, (2018).
Caleffi, M. Optimal Routing for Quantum Networks, IEEE Access, Vol 5, https://doi.org/10.1109/ACCESS.2017.2763325 (2017).
Caleffi, M., Cacciapuoti, A. S. & Bianchi, G. Quantum Internet: from Communication to Distributed Computing, aXiv:1805.04360 (2018).
Castelvecchi, D. The quantum internet has arrived, Nature, News and Comment, https://www.nature.com/articles/d41586-018-01835-3 (2018).
Cuomo, D., Caleffi, M. & Cacciapuoti, A. S. Towards a Distributed Quantum Computing Ecosystem, arXiv:2002.11808v1 (2020).
Chakraborty, K., Rozpedeky, F., Dahlbergz, A. & Wehner, S. Distributed Routing in a Quantum Internet, arXiv:1907.11630v1 (2019).
Khatri, S., Matyas, C. T., Siddiqui, A. U. & Dowling, J. P. Practical figures of merit and thresholds for entanglement distribution in quantum networks. Phys. Rev. Research 1, 023032 (2019).
Kozlowski, W. & Wehner, S. Towards Large-Scale Quantum Networks, Proc. of the Sixth Annual ACM International Conference on Nanoscale Computing and Communication, Dublin, Ireland, arXiv:1909.08396 (2019).
Pathumsoot, P., Matsuo, T., Satoh, T., Hajdusek, M., Suwanna, S. & Van Meter, R. Modeling of Measurement-based Quantum Network Coding on IBMQ Devices. Phys. Rev. A 101, 052301 (2020).
Pal, S., Batra, P., Paterek, T. & Mahesh, T. S. Experimental localisation of quantum entanglement through monitored classical mediator, arXiv:1909.11030v1 (2019).
Miguel-Ramiro, J. & Dur, W. Delocalized information in quantum networks, New J. Phys, https://doi.org/10.1088/1367-2630/ab784d (2020).
Pirker, A. & Dur, W. A quantum network stack and protocols for reliable entanglement-based networks, arXiv:1810.03556v1 (2018).
Miguel-Ramiro, J., Pirker, A. & Dur, W. Genuine quantum networks: superposed tasks and addressing, arXiv:2005.00020v1 (2020).
Amer, O., Krawec, W. O. and Wang, B. Efficient Routing for Quantum Key Distribution Networks, arXiv:2005.12404 (2020).
Liu, Y. Preliminary Study of Connectivity for Quantum Key Distribution Network, arXiv:2004.11374v1 (2020).
Hosen, M. A., Khosravi, A., Nahavandi, S. & Creighton, D. IEEE Transactions on Industrial Electronics 62(no. 7), 4420–4429 (2015).
Precup, R.-E., Angelov, P., Costa, B. S. J. & Sayed-Mouchaweh, M. An overview on fault diagnosis and nature-inspired optimal control of industrial process applications. Computers in Industry 74, 75–94 (2015).
Saadat, J., Moallem, P. & Koofigar, H. Training echo state neural network using harmony search algorithm. International Journal of Artificial Intelligence 15(no. 1), 163–179 (2017).
Vrkalovic, S., Lunca, E.-C. & Borlea, I.-D. Model-free sliding mode and fuzzy controllers for reverse osmosis desalination plants. International Journal of Artificial Intelligence 16(no. 2), 208–222 (2018).
Guerreschi, G. G. & Smelyanskiy. M. Practical optimization for hybrid quantum-classical algorithms, arXiv:1701.01450 (2017).
Pang, C.-Y., Zhou, Z.-W. & Guo, G.-C. Quantum Discrete Cosine Transform for Image Compression, arXiv:quant-ph/0601043 (2006).
Grover, L. K. A fast quantum mechanical algorithm for database search, Proceedings of the 28th ACM symposium on Theory of Computing (STOC), pp.212–218 (1996).
Gyongyosi, L. A Universal Quantum Algorithm for Time Complexity Reduction of Quantum Computers, Proceedings of Quantum Information Processing 2019 (QIP 2019), University of Colorado Boulder, USA (2019).
Roy, A. & Chakraborty, R. S. Camera Source Identification Using Discrete Cosine Transform Residue Features and Ensemble Classifier, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 10.1109/CVPRW.2017.231 (2017).
Freund, Y. & Schapire, R. E. Experiments with a new boosting algorithm, ICML, vol. 96, pp. 148–156. 4 (1996).
Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences. 55, 119 (1997).
Criminisi, A., Shotton, J. & Konukoglu, E. Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning, Microsoft Research Cambridge, Tech. Rep. MSRTR-2011-114, vol. 5, no. 6, p. 12, 3 (2011).
Acknowledgements
Open access funding provided by Budapest University of Technology and Economics (BME). The research reported in this paper has been supported by the Hungarian Academy of Sciences (MTA Premium Postdoctoral Research Program 2019), by the National Research, Development and Innovation Fund (TUDFO/51757/2019-ITM, Thematic Excellence Program), by the National Research Development and Innovation Office of Hungary (Project No. 2017-1.2.1-NKP-2017-00001), by the Hungarian Scientific Research Fund - OTKA K-112125 and in part by the BME Artificial Intelligence FIKP grant of EMMI (Budapest University of Technology, BME FIKP-MI/SC).
Author information
Authors and Affiliations
Contributions
L.GY. designed the protocol and wrote the manuscript. L.GY. and S.I. analyzed the results. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Appendix
Appendix
Algorithm 1
\({\mathscr{L}}\) supervised machine learning control of \({\mathscr{C}}\)
Input: The Ωi coefficients from block \({\mathscr{C}}\).
Output: Classification of the Ωi coefficients, error Θ of \({\mathscr{P}}\); error Δ of \({\mathscr{C}}\), updated Ωi coefficients.
Step 1. Let \({{\mathscr{S}}}_{T}\) be the training set of \({\mathscr{L}}\) as \({{\mathscr{S}}}_{T}=\langle ({\kappa }_{1},{l}_{1}),\ldots ,({\kappa }_{m},{l}_{m})\rangle \), where κi is an i-th instance and an n-length vector (see (48)) drawn from the input space \({\mathscr{X}}\), while \({l}_{i}\in {{\mathscr{S}}}_{\ell }\) is the class label associated with κi, where \({{\mathscr{S}}}_{\ell }\) is the set of labels \({{\mathscr{S}}}_{\ell }=\{1,\ldots ,k\}\).
Step 2. For i = 1, …, m, set the initial D0(i) distribution of the i-th instance of \({{\mathscr{S}}}_{T}\) as \({D}_{0}(i)=\frac{1}{m}\).
Step 3. Set the R iteration number. For an r-th iteration, r = 1, …, R, let Dr be distribution. Define hypothesis (implementable via a weak learning algorithm102,103,104,105) using the input distribution Dr as
such that the εr training error (error of hr) of \({\mathscr{L}}\) is
is minimized with respect to the distribution Dr.
Step 4. If εr ≤ 0.5 goto Step 5, otherwise set R = r − 1, and stop.
Step 5. If hr(κi) = li, set Dr+1(i) as
where χr is a normalization term, while Wr is a weighting coefficient, as
If hr(κi) ≠ li, set
Step 6. Repeat steps 3–5 for R times.
Step 7. From the R hypotheses h1, …, hR, set the final hypothesis H to classify the Ωi coefficients and the input system Y as
and calculate the ε(H) error of H as
where DH is the distribution associated to H.
Step 8. Initialize \({\partial }_{{\mathscr{C}}}\) and \({\eta }_{{\mathscr{C}}}\) parameters as
where
where Y is defined in (28), \({y}_{i}=U({\vec{\theta }}_{i}){x}_{i}\), and
where \({\Omega }_{i}=U({\vec{\theta }{\prime} }_{i}){\tilde{x}}_{i}\).
Step 9. For an i-th coefficient Ωi, set
Step 10. Set σi threshold for an i-th coefficient as
Repeat step 9, until
where ς, ς > 0 is defined via the relation
where B is the number of non-zero elements Ωi of κ, B ≤ n. If σi < ς, goto step 11.
Step 11. Output the Θ error of \({\mathscr{P}}\) as
where ρ is a constant, while ε(H) is given in (107).
Step 12. Output the error Δ of \({\mathscr{C}}\) as
Step 13. According to (116), calibrate the Ωi coefficients (defined in (49)) of output κ in (48) of \({\mathscr{C}}\), to yield \(\Delta < {\mathscr{E}}\) in (115), where \({\mathscr{E}}\) is a threshold on the error of \({\mathscr{P}}\).
Procedure 1 Pre-processing \({\mathscr{P}}\)
Input: Output Y (28) of the non-reduced QG0 reference circuit.
Output: Input \(|\tilde{X}\rangle \) (31) of reduced quantum circuit QG*.
Step 0. Characterize the \({U}_{0}(\vec{\theta })\) (25) unitaries of the QG0 reference circuit via gate parameters (33), and the reference input |X〉 (22) via X (24). Evaluate |Y〉 (29) via Y (28).
Step 1. Fed Y into block \({\mathscr{C}}\) to determine κ (48), and control the results of \({\mathscr{C}}\) via \({\mathscr{L}}\).
Step 2. Output \(\tilde{X}\) via (30) and produce \(|\tilde{X}\rangle \) as (31).
Algorithm 2
Quantum algorithm UR
Input: Output state |Z〉 (36) of QG*.
Output: Quantum state |YR〉 (59).
Step 1. Prepare the reduced quantum state \(|\tilde{X}\rangle \) (30) using \(\tilde{X}\) (31) of \({\mathscr{P}}\).
Step 2. Prepare the unitaries \(U(\vec{\theta }{\prime} )\) of (32) via \(\vec{\theta }{\prime} \) (33) of QG*.
Step 3. Fed \(|\tilde{X}\rangle \) into QG* to yield output system |Z〉 (36).
Step 4. Utilize the quantum register set \({\mathscr{R}}\) (63). Apply UR on \({\mathscr{R}}\) to get |YR〉 (59) in register |R6〉 of \({\mathscr{R}}\).
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Gyongyosi, L., Imre, S. Circuit Depth Reduction for Gate-Model Quantum Computers. Sci Rep 10, 11229 (2020). https://doi.org/10.1038/s41598-020-67014-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-020-67014-5
This article is cited by
-
Comparison of the similarity between two quantum images
Scientific Reports (2022)
-
Fixed-point oblivious quantum amplitude-amplification algorithm
Scientific Reports (2022)
-
An efficient simulation for quantum secure multiparty computation
Scientific Reports (2021)
-
Hybrid quantum investment optimization with minimal holding period
Scientific Reports (2021)
-
Speeding up quantum perceptron via shortcuts to adiabaticity
Scientific Reports (2021)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.