Towards Optimal Topology Aware Quantum Circuit Synthesis

Davis, Marc; Smith, Ethan; Tudor, Ana; Sen, Koushik; Siddiqi, Irfan; Iancu, Costin

doi:10.1109/qce49297.2020.00036

Cited by 53 publications

(39 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To compile the verification circuits, we use the quantum gate synthesis algorithm QSearch [36]. The bit-flip symmetry verification circuit is the same across all instances with the same number of nodes, and the two 4-node instances in our benchmark have the same qubit permutation symmetry verification circuit, enabling us to optimize these circuits only once and use them for multiple different instances.…”

Section: Resultsmentioning

confidence: 99%

Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries

Shaydulin

Galda²

2021

2021 IEEE International Conference on Quantum Computing and Engineering (QCE)

View full text Add to dashboard Cite

High error rates and limited fidelity of quantum gates in near-term quantum devices are the central obstacles to successful execution of the Quantum Approximate Optimization Algorithm (QAOA). In this paper we introduce an applicationspecific approach for mitigating the errors in QAOA evolution by leveraging the symmetries present in the classical objective function to be optimized. Specifically, the QAOA state is projected into the symmetry-restricted subspace, with projection being performed either at the end of the circuit or throughout the evolution. Our approach improves the fidelity of the QAOA state, thereby increasing both the accuracy of the sample estimate of the QAOA objective and the probability of sampling the binary string corresponding to that objective value. We demonstrate the efficacy of the proposed methods on QAOA applied to the MaxCut problem, although our methods are general and apply to any objective function with symmetries, as well as to the generalization of QAOA with alternative mixers. We experimentally verify the proposed methods on an IBM Quantum processor, utilizing up to 5 qubits. When leveraging a global bitflip symmetry, our approach leads to a 23% average improvement in quantum state fidelity.

show abstract

Section: Resultsmentioning

confidence: 99%

Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries

Shaydulin

Galda²

2021

2021 IEEE International Conference on Quantum Computing and Engineering (QCE)

View full text Add to dashboard Cite

show abstract

“…We also want to note that the content of the unitary matrix can be constructed in a native Pythonic way, e.g., via math libraries, such as numpy or scipy [28], or quantum libraries, such as cirq [8], OpenFermion [20], etc. Various unitary synthesis techniques are available in qcor, such as kak [15], qfast [29], qsearch [7], qfactor [27], etc. If not specifically given in the decompose call after the qubit register, as the sample in Fig.…”

Section: Circuit Synthesis Extensionmentioning

confidence: 99%

“…The qcor runtime is retargetable, meaning that the same quantum kernel can be executed on any hardware backends. from qcor import * @qjit def ansatz(q : qreg, t0: float): from qcor import * @qjit def ccnot(q : qreg): # create 111 X(q) # decompose ccnot matrix with decompose(q) as ccnot: ccnot = np.eye(8) ccnot [6,6] = 0.0 ccnot [7,7] = 0.0 ccnot [6,7] = 1.0 ccnot [7,6] = 1.0 Measure(q) # Allocate 3 qubits, run, get result q = qalloc(3) ccnot(q) print(q.counts()) Hence, users just need to use the -qpu flag to select the QPU backend that they want to run the quantum experiments on. With an ideal simulator, the observable converges to the expected ground-state energy of deuteron.…”

Section: Variational Quantum Algorithmsmentioning

confidence: 99%

Extending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation

Nguyen¹,

McCaskey²

2021

Preprint

View full text Add to dashboard Cite

Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. The quantum software community has adopted Python on a number of large-scale efforts due to these characteristics, as well as the remote nature of near-term quantum processors. The use of Python has enabled quick prototyping for quantum code that directly benefits pertinent research and development efforts in quantum scientific computing. However, this rapid prototyping ability comes at the cost of future performant integration for tightly-coupled CPU-QPU architectures with fast-feedback. Here we present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time (QJIT) compilation. Our work builds off the QCOR C++ language extension and compiler infrastructure to enable a single-source, quantum hardwareagnostic approach to quantum-classical computing that retains the performance required for tightly coupled CPU-QPU compute models. We detail this Pythonic extension, its programming model and underlying software architecture, and provide a robust set of examples to demonstrate the utility of our approach.

show abstract

“…Compiling of arbitrary unitaries into a sequence of gates native to a quantum processor has been for instance obtained by an A* inspired algorithm. Such algorithm is conceived for Noisy-Intermediate-Scale Quantum devices era, as it aims to minimize the number of CNOT used while accounting for connectivity [Davis et al, 2020]. Although machine learning approaches can return great quality results and optimized circuits, they usually have high execution-times due to the limited pre-compilation steps that can be employed.…”

Section: Beyond the Solovay-kitaev Theoremmentioning

confidence: 99%

Quantum Compiling

Maronese¹,

Moro²,

Rocutto³

et al. 2021

Preprint

View full text Add to dashboard Cite

Quantum compiling fills the gap between the computing layer of high-level quantum algorithms and the layer of physical qubits with their specific properties and constraints. Quantum compiling is a hybrid between the general-purpose compilers of computers, transforming high-level language to assembly language and hardware synthesis by hardware description language, where functions are automatically synthesized into customized hardware. Here we review the quantum compiling stack of both gate model quantum computers and the adiabatic quantum computers, respectively. The former involves low level qubit control, quantum error correction, synthesis of short quantum circuits, transpiling, while the latter involves the virtualization of qubits by embedding of QUBO and HUBO problems on constrained graphs of physical qubits and both quantum error suppression and correction. Commercial initiatives and quantum compiling products are reviewed, including explicit programming examples.

show abstract

Towards Optimal Topology Aware Quantum Circuit Synthesis

Cited by 53 publications

References 33 publications

Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries

Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries

Extending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation

Quantum Compiling

Contact Info

Product

Resources

About