Benchmarking Quantum Circuit Transformation with QKNOB Circuits
- URL: http://arxiv.org/abs/2301.08932v2
- Date: Fri, 20 Dec 2024 20:30:16 GMT
- Title: Benchmarking Quantum Circuit Transformation with QKNOB Circuits
- Authors: Sanjiang Li, Xiangzhen Zhou, Yuan Feng,
- Abstract summary: superconducting quantum devices impose strict connectivity constraints on quantum circuit execution.<n>This paper introduces QKNOB, a novel benchmark construction method for quantum circuit transformation.<n>We show that SABRE, the default Qiskit compiler, consistently achieves the best performance on the 53-qubit IBM Q Rochester and Google Sycamore devices.
- Score: 4.518076543914809
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Current superconducting quantum devices impose strict connectivity constraints on quantum circuit execution, necessitating circuit transformation before executing quantum circuits on physical hardware. Numerous quantum circuit transformation (QCT) algorithms have been proposed. To enable faithful evaluation of state-of-the-art QCT algorithms, this paper introduces QKNOB (Qubit mapping Benchmark with Known Near-Optimality), a novel benchmark construction method for QCT. QKNOB circuits have built-in transformations with near-optimal (close to the theoretical optimum) SWAP count and depth overhead. QKNOB provides general and unbiased evaluation of QCT algorithms. Using QKNOB, we demonstrate that SABRE, the default Qiskit compiler, consistently achieves the best performance on the 53-qubit IBM Q Rochester and Google Sycamore devices for both SWAP count and depth objectives. Our results also reveal significant performance gaps relative to the near-optimal transformation costs of QKNOB. Our construction algorithm and benchmarks are open-source.
Related papers
- Topology-Driven Quantum Architecture Search Framework [2.9862856321580895]
We propose a Topology-Driven Quantum Architecture Search (TD-QAS) framework to identify high-performance quantum circuits.
By decoupling the extensive search space into topology and gate-type components, TD-QAS avoids exploring gate configurations within low-performance topologies.
arXiv Detail & Related papers (2025-02-20T05:05:53Z) - Block encoding by signal processing [0.0]
We demonstrate that QSP-based techniques, such as Quantum Singular Value Transformation (QSVT) and Quantum Eigenvalue Transformation for Unitary Matrices (QETU) can themselves be efficiently utilized for BE implementation.
We present several examples of using QSVT and QETU algorithms, along with their combinations, to block encode Hamiltonians for lattice bosons.
We find that, while using QSVT for BE results in the best gate count scaling with the number of qubits per site, LOVE-LCU outperforms all other methods for operators acting on up to $lesssim11$ qubits.
arXiv Detail & Related papers (2024-08-29T18:00:02Z) - Route-Forcing: Scalable Quantum Circuit Mapping for Scalable Quantum Computing Architectures [41.39072840772559]
Route-Forcing is a quantum circuit mapping algorithm that shows an average speedup of $3.7times$.
We present a quantum circuit mapping algorithm that shows an average speedup of $3.7times$ compared to the state-of-the-art scalable techniques.
arXiv Detail & Related papers (2024-07-24T14:21:41Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - Reinforcement learning-assisted quantum architecture search for variational quantum algorithms [0.0]
This thesis focuses on identifying functional quantum circuits in noisy quantum hardware.
We introduce a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently.
In dealing with various VQAs, our RL-based QAS outperforms existing QAS.
arXiv Detail & Related papers (2024-02-21T12:30:39Z) - KetGPT -- Dataset Augmentation of Quantum Circuits using Transformers [1.236829197968612]
Quantum algorithms, represented as quantum circuits, can be used as benchmarks for assessing the performance of quantum systems.
Random circuits are, however, not representative benchmarks as they lack the inherent properties of real quantum algorithms.
This research aims to enhance the existing quantum circuit datasets by generating what we refer to as realistic-looking' circuits.
arXiv Detail & Related papers (2024-02-20T20:02:21Z) - Learning Quantum Phase Estimation by Variational Quantum Circuits [0.9208007322096533]
We develop a variational quantum circuit (VQC) approximation to reduce the depth of the Quantum Phase Estimation circuit.
Our experiments demonstrated that the VQC outperformed both Noisy QPE and standard QPE on real hardware by reducing circuit noise.
This VQC integration into quantum compilers holds significant promise for quantum algorithms with deep circuits.
arXiv Detail & Related papers (2023-11-08T13:57:24Z) - Indirect Quantum Approximate Optimization Algorithms: application to the
TSP [1.1786249372283566]
Quantum Alternating Operator Ansatz takes into consideration a general parameterized family of unitary operators to efficiently model the Hamiltonian describing the set of vectors.
This algorithm creates an efficient alternative to QAOA, where: 1) a Quantum parametrized circuit executed on a quantum machine models the set of string vectors; 2) a Classical meta-optimization loop executed on a classical machine; 3) an estimation of the average cost of each string vector computing.
arXiv Detail & Related papers (2023-11-06T17:39:14Z) - Scaling Limits of Quantum Repeater Networks [62.75241407271626]
Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing.
Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability.
In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
arXiv Detail & Related papers (2023-05-15T14:57:01Z) - GASP -- A Genetic Algorithm for State Preparation [0.0]
We present a genetic algorithm for state preparation (GASP) which generates relatively low-depth quantum circuits for initialising a quantum computer in a specified quantum state.
GASP can produce more efficient circuits of a given accuracy with lower depth and gate counts than other methods.
arXiv Detail & Related papers (2023-02-22T04:41:01Z) - Evaluation of Parameterized Quantum Circuits with Cross-Resonance
Pulse-Driven Entanglers [0.27998963147546146]
Variational Quantum Algorithms (VQAs) have emerged as a powerful class of algorithms that is highly suitable for noisy quantum devices.
Previous works have shown that choosing an effective parameterized quantum circuit (PQC) or ansatz for VQAs is crucial to their overall performance.
In this paper, we utilize pulse-level access to quantum machines and our understanding of their two-qubit interactions to optimize the design of two-qubit entanglers.
arXiv Detail & Related papers (2022-11-01T09:46:34Z) - Compilation of algorithm-specific graph states for quantum circuits [55.90903601048249]
We present a quantum circuit compiler that prepares an algorithm-specific graph state from quantum circuits described in high level languages.
The computation can then be implemented using a series of non-Pauli measurements on this graph state.
arXiv Detail & Related papers (2022-09-15T14:52:31Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity.
We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree.
These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
arXiv Detail & Related papers (2022-01-06T21:02:30Z) - Quantum circuit architecture search on a superconducting processor [56.04169357427682]
Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry.
However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability.
We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
arXiv Detail & Related papers (2022-01-04T01:53:42Z) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - Supervised Learning Enhanced Quantum Circuit Transformation [6.72166630054365]
A quantum circuit transformation (QCT) is required when executing a quantum program in a real quantum processing unit (QPU)
We propose a framework that uses a policy artificial neural network (ANN) trained by supervised learning on shallow circuits to help existing QCT algorithms select the most promising SWAP gate.
arXiv Detail & Related papers (2021-10-06T20:32:28Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow [7.619626059034881]
We propose an efficient scheme for quantum circuit equivalence checking.
The proposed scheme allows to verify even large circuit instances with tens of thousands of operations within seconds or even less.
arXiv Detail & Related papers (2020-09-04T19:58:53Z) - QUANTIFY: A framework for resource analysis and design verification of
quantum circuits [69.43216268165402]
QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits.
It is based on Google Cirq and is developed with Clifford+T circuits in mind.
For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
arXiv Detail & Related papers (2020-07-21T15:36:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.