arXiv subjects

Koen Bertels

Publications and source records attributed to Koen Bertels.

15 recordsLinked to original sources

arXiv2024

A resource-efficient variational quantum algorithm for mRNA codon optimization

Optimizing the mRNA codon has an essential impact on gene expression for a specific target protein. It is an NP-hard problem; thus, exact solutions to such optimization problems become computationally intractable for realistic problem sizes on both classical and quantum computers. However, approximate solutions via heuristics can substantially impact the application they enable. Quantum approximate optimization is an alternative computation paradigm promising for tackling such problems. Recently, there has been some research in quantum algorithms for bioinformatics, specifically for mRNA codon optimization. This research presents a denser way to encode codons for implementing mRNA codon optimization via the variational quantum eigensolver algorithms on a gate-based quantum computer. This reduces the qubit requirement by half compared to the existing quantum approach, thus allowing longer sequences to be executed on existing quantum processors. The performance of the proposed algorithm is evaluated by comparing its results to exact solutions, showing well-matching results.

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arXiv2023

"Toward" Metal-Organic Framework Design by Quantum Computing

The article summarizes the study performed in the context of the Deloitte Quantum Climate Challenge in 2023. We present a hybrid quantum-classical method for calculating Potential Energy Surface scans, which are essential for designing Metal-Organic Frameworks for Direct Air Capture applications. The primary objective of this challenge was to highlight the potential advantages of employing quantum computing. To evaluate the performance of the model, we conducted total energy calculations using various computing frameworks and methods. The results demonstrate, at a small scale, the potential advantage of quantum computing-based models. We aimed to define relevant classical computing model references for method benchmarking. The most important benefits of using the PISQ approach for hybrid quantum-classical computational model development and assessment are demonstrated.

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arXiv2021

QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography

In this research, we extend the universal reinforcement learning (URL) agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory on density matrices. Quantum process tomography (QPT) algorithms form the tractable subset of programs for modeling environmental dynamics. The optimal QPT policy is selected based on a mutable cost function based on algorithmic complexity as well as computational resource complexity. Instead of Turing machines, we estimate the cost metrics on a high-level language to allow realistic experimentation. The entire agent design is encapsulated in a self-replicating quine which mutates the cost function based on the predictive value of the optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT policies evolve using genetic programming, mimicking the development of physical theories each with different resource trade-offs. This formal framework is termed Quantum Knowledge Seeking Agent (QKSA). Despite its importance, few quantum reinforcement learning models exist in contrast to the current thrust in quantum machine learning. QKSA is the first proposal for a framework that resembles the classical URL models. Similar to how AIXI-tl is a resource-bounded active version of Solomonoff universal induction, QKSA is a resource-bounded participatory observer framework to the recently proposed algorithmic information-based reconstruction of quantum mechanics. QKSA can be applied for simulating and studying aspects of quantum information theory. Specifically, we demonstrate that it can be used to accelerate quantum variational algorithms which include tomographic reconstruction as its integral subroutine.

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arXiv2021

Quantum Computing -- A new scientific revolution in the making

Given the impending timeline of developing good-quality quantum processing units, it is time to rethink the approach to advance quantum computing research. Rather than waiting for quantum hardware technologies to mature, we need to start assessing in tandem the impact of the occurrence of quantum computing, or rather Quantum Computing Logic (QC-Logic), on various scientific fields. This is where the subtitle comes from. A new scientific revolution is unfolding. In making real scientific progress, we need to use an additional and complementary approach, which the NISQ program or any follow-up approach does not propose. We must be aware that defining, implementing, and testing quantum concepts in any field is tremendous work. The main reason is that QC initiates an overall revolution in all scientific fields, and how those machines will be used in daily life is a very big challenge. That is why we propose a complete update of the first PISQ paper. We still advocate the additional PISQ approach: Perfect Intermediate-Scale Quantum computing based on a well-established concept of perfect qubits. We expand the quantum road map with (N)FTQC, which stands for (Non) Fault-Tolerant Quantum Computing. This will allow researchers to focus exclusively on developing new applications by defining the algorithms in terms of perfect qubits and evaluating them in two ways. Either executed on quantum computing simulators executed on supercomputers or hardware-based qubit chips. This approach will be explained in this paper. Our planet needs a long-term vision and solution. It will enable universities and companies alike to accelerate the development of new quantum algorithms, build the necessary know-how, and thus address one of the key bottlenecks within the quantum industry: the lack of talent to develop well-tested quantum applications.

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arXiv2020

ACSS-q: Algorithmic complexity for short strings via quantum accelerated approach

In this research we present a quantum circuit for estimating algorithmic complexity using the coding theorem method. This accelerates inferring algorithmic structure in data for discovering causal generative models. The computation model is restricted in time and space resources to make it computable in approximating the target metrics. The quantum circuit design based on our earlier work that allows executing a superposition of automata is presented. As a use-case, an application framework for protein-protein interaction ontology based on algorithmic complexity is proposed. Using small-scale quantum computers, this has the potential to enhance the results of classical block decomposition method towards bridging the causal gap in entropy based methods.

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arXiv2020

Quantum circuit design for universal distribution using a superposition of classical automata

In this research, we present a quantum circuit design and implementation for a parallel universal linear bounded automata. This circuit is able to accelerate the inference of algorithmic structures in data for discovering causal generative models. The computation model is practically restricted in time and space resources. A classical exhaustive enumeration of all possible programs on the automata is shown for a couple of example cases. The precise quantum circuit design that allows executing a superposition of programs, along with a superposition of inputs as in the standard quantum Turing machine formulation, is presented. This is the first time, a superposition of classical automata is implemented on the circuit model of quantum computation, having the corresponding mechanistic parts of a classical Turing machine. The superposition of programs allows our model to be used for experimenting with the space of program-output behaviors in algorithmic information theory. Our implementations on OpenQL and Qiskit quantum programming language is copy-left and is publicly available on GitHub.

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arXiv2020

Realistic simulation of quantum computation using unitary and measurement channels

The implementation and practicality of quantum algorithms highly hinge on the quality of operations within a quantum processor. Therefore, including realistic error models in quantum computing simulation platforms is crucial for testing these algorithms. Existing classical simulation techniques of quantum information processing devices exhibit a trade-off between scalability (number of qubits that can be simulated) and accuracy (how close the simulation is to the target error model). In this paper, we introduce a new simulation approach that relies on approximating the density matrix evolution by a stochastic sum of unitary and measurement channels within a pure state simulation environment. This model shows an improvement of at least one order of magnitude in terms of accuracy compared to the best known stochastic approaches while allowing to simulate a larger number of qubits compared to the exact density matrix simulation. Furthermore, we used this approach to realistically simulate the Grover's algorithm and the surface code 17 using gate set tomography characterization of quantum operations as a noise model.

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arXiv2020

QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction

In this article, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms. Each one of the four steps of the implementation (TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept examples to target both the genomics research community and quantum application developers in a self-contained manner. The details of the implementation are discussed for the various layers of the quantum full-stack accelerator design. We also highlight the limitations of current classical simulation and available quantum hardware systems. The implementation is open-source and can be found on https://github.com/prince-ph0en1x/QuASeR.

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arXiv2020

Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability

An active area of investigation in the search for quantum advantage is Quantum Machine Learning. Quantum Machine Learning, and Parameterized Quantum Circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space. But is the ability of a quantum circuit to uniformly address the Hilbert space a good indicator of classification accuracy? In our work, we use methods and quantifications from prior art to perform a numerical study in order to evaluate the level of correlation. We find a strong correlation between the ability of the circuit to uniformly address the Hilbert space and the achieved classification accuracy for circuits that entail a single embedding layer followed by 1 or 2 circuit designs. This is based on our study encompassing 19 circuits in both 1 and 2 layer configuration, evaluated on 9 datasets of increasing difficulty. Future work will evaluate if this holds for different circuit designs.

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arXiv2019

Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attention of the current research is directed to algorithmic research on artificial data that is disconnected from live systems, such as optimization of systems or training of learning algorithms. In this paper we investigate the integration of quantum systems into industry-grade system architectures. In this work we propose a system architecture for the integration of quantum accelerators. In order to evaluate our proposed system architecture we implemented various algorithms including a classical system, a gate-based quantum accelerator and a quantum annealer. This algorithm automates user habits using data-driven functions trained on real-world data. This also includes an evaluation of the quantum enhanced kernel, that previously was only evaluated on artificial data. In our evaluation, we showed that the quantum-enhanced kernel performs at least equally well to a classical state-of-the-art kernel. We also showed a low reduction in accuracy and latency numbers within acceptable bounds when running on the gate-based IBM quantum accelerator. We, therefore, conclude it is feasible to integrate NISQ-era devices in industry-grade system architecture in preparation for future hardware improvements.

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arXiv2019

An algorithm for DNA read alignment on quantum accelerators

With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors allow us to efficiently compute important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of big data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, that uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover's search algorithm in two ways to allow for: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX simulator framework. This represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design. The open-source implementation can be found on https://github.com/prince-ph0en1x/QAGS.

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arXiv2019

Decoding surface code with a distributed neural network based decoder

There has been a rise in decoding quantum error correction codes with neural network based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that, successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the Renormalization Group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the Blossom algorithm and a neural network based decoder. We show that similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network based decoders.

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arXiv2018

Comparing neural network based decoders for the surface code

Matching algorithms can be used for identifying errors in quantum systems, being the most famous the Blossom algorithm. Recent works have shown that small distance quantum error correction codes can be efficiently decoded by employing machine learning techniques based on neural networks (NN). Various NN-based decoders have been proposed to enhance the decoding performance and the decoding time. Their implementation differs in how the decoding is performed, at logical or physical level, as well as in several neural network related parameters. In this work, we implement and compare two NN-based decoders, a low level decoder and a high level decoder, and study how different NN parameters affect their decoding performance and execution time. Crucial parameters such as the size of the training dataset, the structure and the type of the neural network, and the learning rate used during training are discussed. After performing this comparison, we conclude that the high level decoder based on a Recurrent NN shows a better balance between decoding performance and execution time and it is much easier to train. We then test its decoding performance for different code distances, probability datasets and under the depolarizing and circuit error models.

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arXiv2018

Code Deformation and Lattice Surgery Are Gauge Fixing

The large-scale execution of quantum algorithms requires basic quantum operations to be implemented fault-tolerantly. The most popular technique for accomplishing this, using the devices that can be realised in the near term, uses stabilizer codes which can be embedded in a planar layout. The set of fault-tolerant operations which can be executed in these systems using unitary gates is typically very limited. This has driven the development of measurement-based schemes for performing logical operations in these codes, known as lattice surgery and code deformation. In parallel, gauge fixing has emerged as a measurement-based method for performing universal gate sets in subsystem stabilizer codes. In this work, we show that lattice surgery and code deformation can be expressed as special cases of gauge fixing, permitting a simple and rigorous test for fault-tolerance together with simple guiding principles for the implementation of these operations. We demonstrate the accuracy of this method numerically with examples based on the surface code, some of which are novel.

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arXiv2017

Decoding Small Surface Codes with Feedforward Neural Networks

Surface codes reach high error thresholds when decoded with known algorithms, but the decoding time will likely exceed the available time budget, especially for near-term implementations. To decrease the decoding time, we reduce the decoding problem to a classification problem that a feedforward neural network can solve. We investigate quantum error correction and fault tolerance at small code distances using neural network-based decoders, demonstrating that the neural network can generalize to inputs that were not provided during training and that they can reach similar or better decoding performance compared to previous algorithms. We conclude by discussing the time required by a feedforward neural network decoder in hardware.

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