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Codesign, an integral part of computer architecture referring to the information interaction in hardware-software stack, is able to boost the algorithm mapping and execution in the computer hardware. This well applies to the noisy intermediate-scale quantum era, where quantum algorithms and quantum processors both need to be shaped to allow for advantages in experimental implementations. The state-of-the-art quantum adiabatic optimization algorithm faces challenges for scaling up, where the deteriorating optimization performance is not necessarily alleviated by increasing the circuit depth given the noise in the hardware. The counterdiabatic term can be introduced to accelerate the convergence, but decomposing the unitary operator corresponding to the counterdiabatic terms into one and two-qubit gates may add additional burden to the digital circuit depth. In this work, we focus on the counterdiabatic protocol with a codesigned approach to implement this algorithm on a photonic quantum processor. The tunable Mach-Zehnder interferometer mesh provides rich programmable parameters for local and global manipulation, making it able to perform arbitrary unitary evolutions. Accordingly, we directly implement the unitary operation associated to the counterdiabatic quantum optimization on our processor without prior digitization. Furthermore, we develop and implement an optimized counterdiabatic method by tackling the higher-order many-body interaction terms. Moreover, we benchmark the performance in the case of factorization, by comparing the final success probability and the convergence speed. In conclusion, we experimentally demonstrate the advantages of a codesigned mapping of counterdiabatic quantum dynamics for quantum computing on photonic platforms., Comment: 9 pages, 4 figures. Comments are welcome

We present an enhanced bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm to address higher-order unconstrained binary optimization (HUBO) problems. Combinatorial optimization plays a crucial role in many industrial applications, yet classical computing often struggles with complex instances. By encoding these problems as Ising spin glasses and leveraging the advancements in quantum computing technologies, quantum optimization methods emerge as a promising alternative. We apply BF-DCQO with an enhanced bias term to a HUBO problem featuring three-local terms in the Ising spin-glass model. Our protocol is experimentally validated using 156 qubits on an IBM quantum processor with a heavy-hex architecture. In the studied instances, the results outperform standard methods, including the quantum approximate optimization algorithm (QAOA), quantum annealing, simulated annealing, and Tabu search. Furthermore, we perform an MPS simulation and provide numerical evidence of the feasibility of a similar HUBO problem on a 433-qubit Osprey-like quantum processor. Both studied cases, the experiment on 156 qubits and the simulation on 433 qubits, can be considered as the start of the commercial quantum advantage era, Kipu dixit, and even more when extended soon to denser industry-level HUBO problems., Comment: 12 pages, 5 figures

Quantum noise reduction below the shot noise limit is a signature of light-matter quantum interaction. A limited amount of squeezing can be obtained along the transient evolution of a two-level system resonantly interacting with a harmonic mode. We propose the use of optimal quantum control over the two-level system to enhance the transient noise reduction in the harmonic mode in a system described by the Jaynes-Cummings model. Specifically, we propose the use of a sequence of Gaussian pulses in a given time window. We find that the correct choice of pulse times can reduce the noise in the quadrature field mode well below the shot noise, reaching reductions of over 80$\%$. As the Jaynes-Cummings model describes a pivotal light-matter quantum system, our approach for noise reduction provides an experimentally feasible protocol to produce a non-trivial amount of squeezing with current technology., Comment: 6 + 7 pages, 6 figures

This paper is focused on the segregation of FGK dwarf and giant stars through narrow-band photometric data using the Spanish Virtual Observatory (SVO) Filter Profile Service and associated photometric tools. We selected spectra from the MILES, STELIB, and ELODIE stellar libraries, and used SVO photometric tools to derive the synthetic photometry in 15 J-PAS narrow filters, which were especially selected to cover spectral features sensitive to gravity changes. Using machine-learning techniques as the Gaussian mixture model and the support vector machine, we defined several criteria based on J-PAS colours to discriminate between dwarf and giant stars. We selected five colour-colour diagrams that presented the most promising separation between both samples. Our results show an overall accuracy in the studied sample of $\sim$0.97 for FGK stars, although a dependence on the luminosity type and the stellar effective temperature was found. We also defined a colour-temperature relation for dwarf stars with effective temperatures between 4\,000 and 7\,000\,K, which allows one to estimate the stellar effective temperature from four J-PAS filters ($J0450$, $J0510$, $J0550$, and $J0620$). Additionally, we extended the study to M-type giant and dwarf stars, achieving a similar accuracy to that for FGK stars., Comment: 19 pages, 18 figures, accepted for publication in A&A

We study a job shop scheduling problem for an automatized robot in a high-throughput laboratory and a travelling salesperson problem with recently proposed digitized counterdiabatic quantum optimization (DCQO) algorithms. In DCQO, we find the solution of an optimization problem via an adiabatic quantum dynamics, which is accelerated with counterdiabatic protocols. Thereafter, we digitize the global unitary to encode it in a digital quantum computer. For the job-shop scheduling problem, we aim at finding the optimal schedule for a robot executing a number of tasks under specific constraints, such that the total execution time of the process is minimized. For the traveling salesperson problem, the goal is to find the path that covers all cities and is associated with the shortest traveling distance. We consider both hybrid and pure versions of DCQO algorithms and benchmark the performance against digitized quantum annealing and the quantum approximate optimization algorithm (QAOA). In comparison to QAOA, the DCQO solution is improved by several orders of magnitude in success probability using the same number of two-qubit gates. Moreover, we experimentally implement our algorithms on superconducting and trapped-ion quantum processors. Our results demonstrate that circuit compression using counterdiabatic protocols is amenable to current NISQ hardware and can solve logistics scheduling problems, where other digital quantum algorithms show insufficient performance., Comment: 14 pages, 9 figures

We propose analog counterdiabatic quantum computing (ACQC) to tackle combinatorial optimization problems on neutral-atom quantum processors. While these devices allow for the use of hundreds of qubits, adiabatic quantum computing struggles with non-adiabatic errors, which are inevitable due to the hardware's restricted coherence time. We design counterdiabatic protocols to circumvent those limitations via ACQC on analog quantum devices with ground-Rydberg qubits. To demonstrate the effectiveness of our paradigm, we experimentally apply it to the maximum independent set (MIS) problem with up to 100 qubits and show an enhancement in the approximation ratio with a short evolution time. We believe ACQC establishes a path toward quantum advantage for a variety of industry use cases., Comment: 20 pages, 4 figures

We introduce a method for solving combinatorial optimization problems on digital quantum computers, where we incorporate auxiliary counterdiabatic (CD) terms into the adiabatic Hamiltonian, while integrating bias terms derived from an iterative digitized counterdiabatic quantum algorithm. We call this protocol bias-field digitized counterdiabatic quantum optimization (BF-DCQO). Designed to effectively tackle large-scale combinatorial optimization problems, BF-DCQO demonstrates resilience against the limitations posed by the restricted coherence times of current quantum processors and shows clear enhancement even in the presence of noise. Additionally, our purely quantum approach eliminates the dependency on classical optimization required in hybrid classical-quantum schemes, thereby circumventing the trainability issues often associated with variational quantum algorithms. Through the analysis of an all-to-all connected general Ising spin-glass problem, we exhibit a polynomial scaling enhancement in ground state success probability compared to traditional DCQO and finite-time adiabatic quantum optimization methods. Furthermore, it achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude, and offers an average 1.3x better approximation ratio than the quantum approximate optimization algorithm for the problem sizes studied. We validate these findings through experimental implementations on both trapped-ion quantum computers and superconducting processors, tackling a maximum weighted independent set problem with 36 qubits and a spin-glass on a heavy-hex lattice with 100 qubits, respectively. These results mark a significant advancement in gate-based quantum computing, employing a fully quantum algorithmic approach., Comment: Main text: 5 pages, 3 figures. Supplementary: 11 pages, 12 figures

We introduce a hardware-specific, problem-dependent digital-analog quantum algorithm of a counterdiabatic quantum dynamics tailored for optimization problems. Specifically, we focus on trapped-ion architectures, taking advantage from global M{\o}lmer-S{\o}rensen gates as the analog interactions complemented by digital gates, both of which are available in the state-of-the-art technologies. We show an optimal configuration of analog blocks and digital steps leading to a substantial reduction in circuit depth compared to the purely digital approach. This implies that, using the proposed encoding, we can address larger optimization problem instances, requiring more qubits, while preserving the coherence time of current devices. Furthermore, we study the minimum gate fidelity required by the analog blocks to outperform the purely digital simulation, finding that it is below the best fidelity reported in the literature. To validate the performance of the digital-analog encoding, we tackle the maximum independent set problem, showing that it requires fewer resources compared to the digital case. This hybrid co-design approach paves the way towards quantum advantage for efficient solutions of quantum optimization problems.

We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improving the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines non-trainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings demonstrate the relevance of digital-analog encoding, paving the way for surpassing classical models in image recognition approaching us to quantum-advantage regimes., Comment: 7 pages, 3 figures