arXiv subjects

Alexandru Paler

Publications and source records attributed to Alexandru Paler.

At least 37 records · Page 2Linked to original sources

arXiv2024

Efficient Quantum Circuit Design with a Standard Cell Approach, with an Application to Neutral Atom Quantum Computers

We design quantum circuits by using the standard cell approach borrowed from classical circuit design, which can speed-up the layout of circuits with a regular structure. Our standard cells are general and can be used for all types of quantum circuits: error-corrected or not. The standard cell approach enables the formulation of layout-aware routing algorithms. Our method is directly applicable to neutral atom quantum computers supporting qubit shuttling. Such computers enable zoned architectures for memory, processing and measurement, and we design circuits using qubit storages (memory and measurement zones) and standard cells (processing zones). Herein, we use cubic standard cells for Toffoli gates and, starting from a 3D architecture, we design a multiplication circuit. We present evidence that, when compared with automatic routing methods, our layout-aware routers are significantly faster and achieve shallower 3D circuits (by at least 2.5x) and with a lower routing cost. Additionally, our co-design approach can be used to estimate the resources necessary for a quantum computation without using complex compilation methods. We conclude that standard cells, with the support of layout-aware routing, pave the way to very large scale methods for quantum circuit compilation.

quant-ph↗

arXiv2024

On the Need for Extensible Quantum Compilers with Verification

In this position paper, we posit that a major Department of Energy (DOE)-funded open-source quantum compilation platform is needed to facilitate: (a) resource optimization at the fault-tolerant layer of the quantum computing software stack, and (b) co-design of that layer of the stack with other layers, and that this platform needs to be extensible and include verification.

quant-ph↗

arXiv2023

Transversal Injection: Using the Surface Code to Prepare Non-Pauli Eigenstates

The development of quantum computing systems for large scale algorithms requires targeted error rates unachievable through hardware advancements alone. Quantum Error Correction (QEC) allows us to use systems with a large number of physical qubits to form a fault tolerant system with a lower number of logical qubits and a favourable logical error rate. While some gates can be easily implemented in a QEC code transversally, there is no code that has a universal set of transversal gates. Transversal Injection is a new method of preparing logical non-Pauli eigenstates that can be used as resource states for quantum computation. State preparation can be done directly in the surface code and has the potential to prepare higher fidelity injected states. Compared to other techniques, transversal injection can reduce the resource burden for state distillation protocols. In this paper, the authors present the theory behind this new technique as well as an algorithm for calculating the resulting logical states prepared in the surface code.

quant-ph↗

arXiv2023

Pipelined correlated minimum weight perfect matching of the surface code

We describe a pipeline approach to decoding the surface code using minimum weight perfect matching, including taking into account correlations between detection events. An independent no-communication parallelizable processing stage reweights the graph according to likely correlations, followed by another no-communication parallelizable stage for high confidence matching. A later general stage finishes the matching. This is a simplification of previous correlated matching techniques which required a complex interaction between general matching and re-weighting the graph. Despite this simplification, which gives correlated matching a better chance of achieving real-time processing, we find the logical error rate practically unchanged. We validate the new algorithm on the fully fault-tolerant toric, unrotated, and rotated surface codes, all with standard depolarizing noise. We expect these techniques to be applicable to a wide range of other decoders.

quant-ph↗

arXiv2023

Cost Explosion for Efficient Reinforcement Learning Optimisation of Quantum Circuits

Large scale optimisation of quantum circuits is a computationally challenging problem. Reinforcement Learning (RL) is a recent approach for learning strategies to optimise quantum circuits by increasing the reward of an optimisation agent. The reward is a function of the quantum circuit costs, such as gate and qubit counts, or circuit depth. Our goal is to improve the agent's optimization strategy, by including hints about how quantum circuits are optimized manually: there are situations when the cost of a circuit should be allowed to temporary explode, before applying optimisations which significantly reduce the circuit's cost. We bring numerical evidence, using Bernstein-Vazirani circuits, to support the advantage of this strategy. Our results are preliminary, and show that allowing cost explosions offers significant advantages for RL training, such as reaching optimum circuits. Cost explosion strategies have the potential to be an essential tool for RL of large-scale quantum circuit optimisation.

quant-ph↗

arXiv2023

Testing the Accuracy of Surface Code Decoders

Large-scale, fault-tolerant quantum computations will be enabled by quantum error-correcting codes (QECC). This work presents the first systematic technique to test the accuracy and effectiveness of different QECC decoding schemes by comparing a look-up table decoder to solutions generated using algorithmic decoders. Specifically, we examine the results of minimum-weight-perfect-matching and belief-propagation decoders against exhaustive look-up tables for surface codes up to distance seven and categorise where errors are accurately corrected in both decoding schemes. While our results are preliminary, we show that significant quantitative results can be generated, comparing how actual error channels are successfully or unsuccessfully decoded. We show that different decoding schemes perform very differently under the same QECC scheme and error model, and detail how decoders can be tested and classified with respect to errors that are successfully decodable. This work paves the way to the data driven tuning of decoder ensembles and will enable tailored design of hybrid decoding schemes that allow for real-time decoding, while maintaining the high theoretical thresholds allowed by specific quantum error correction codes.

quant-ph↗

arXiv2023

Towards Faster Reinforcement Learning of Quantum Circuit Optimization: Exponential Reward Functions

Reinforcement learning for the optimization of quantum circuits uses an agent whose goal is to maximize the value of a reward function that decides what is correct and what is wrong during the exploration of the search space. It is an open problem how to formulate reward functions that lead to fast and efficient learning. We propose an exponential reward function which is sensitive to structural properties of the circuit. We benchmark our function on circuits with known optimal depths, and conclude that our function is reducing the learning time and improves the optimization. Our results are a next step towards fast, large scale optimization of quantum circuits.

quant-ph↗

arXiv2023

A T-depth two Toffoli gate for 2D square lattice architectures

We present a novel Clifford+T decomposition of a Toffoli gate. Our decomposition requires no SWAP gates in order to be implemented on 2D square lattices of qubits. This decomposition enables shallower, more fault-tolerant quantum computations on both NISQ and error-corrected architectures. We present the derivation of the circuit, and illustrate the qubit mapping on a Sycamore-like architecture.

quant-ph↗

arXiv2023

Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation

Reinforcement learning (RL) is a promising method for quantum circuit optimisation. However, the state space that has to be explored by an RL agent is extremely large when considering all the possibilities in which a quantum circuit can be transformed through local rewrite operations. This state space explosion slows down the learning of RL-based optimisation strategies. We present for the first time how to use graph neural network (GNN) autoencoders for the optimisation of quantum circuits. We construct directed acyclic graphs from the quantum circuits, encode the graphs and use the encodings to represent RL states. We illustrate our proof of concept implementation on Bernstein-Vazirani circuits and, from preliminary results, we conclude that our autoencoder approach: a) maintains the optimality of the original RL method; b) reduces by 20 \% the size of the table that encodes the learned optimisation strategy. Our method is the first realistic first step towards very large scale RL quantum circuit optimisation.

quant-ph↗

arXiv2022

Quantum computation on a 19-qubit wide 2d nearest neighbour qubit array

In this paper, we explore the relationship between the width of a qubit lattice constrained in one dimension and physical thresholds for scalable, fault-tolerant quantum computation. To circumvent the traditionally low thresholds of small fixed-width arrays, we deliberately engineer an error bias at the lowest level of encoding using the surface code. We then address this engineered bias at a higher level of encoding using a lattice-surgery surface code bus that exploits this bias, or a repetition code to make logical qubits with unbiased errors out of biased surface code qubits. Arbitrarily low error rates can then be reached by further concatenating with other codes, such as Steane [[7,1,3]] code and the [[15,7,3]] CSS code. This enables a scalable fixed-width quantum computing architecture on a square qubit lattice that is only 19 qubits wide, given physical qubits with an error rate of $8.0\times 10^{-4}$. This potentially eases engineering issues in systems with fine qubit pitches, such as quantum dots in silicon or gallium arsenide.

quant-ph↗

arXiv2022

Compilation of algorithm-specific graph states for quantum circuits

We present a quantum circuit compiler that prepares an algorithm-specific graph state from quantum circuits described in high level languages, such as Cirq and Q#. The computation can then be implemented using a series of non-Pauli measurements on this graph state. By compiling the graph state directly instead of starting with a standard lattice cluster state and preparing it over the course of the computation, we are able to better understand the resource costs involved and eliminate wasteful Pauli measurements on the actual quantum device. Access to this algorithm-specific graph state also allows for optimisation over locally equivalent graph states to implement the same quantum circuit. The compiler presented here finds ready application in measurement based quantum computing, NISQ devices and logical level compilation for fault tolereant implementations.

quant-ph↗

arXiv2022

Transversal Injection: A method for direct encoding of ancilla states for non-Clifford gates using stabiliser codes

Fault-tolerant, error-corrected quantum computation is commonly acknowledged to be crucial to the realisation of large-scale quantum algorithms that could lead to extremely impactful scientific or commercial results. Achieving a universal set of quantum gate operations in a fault-tolerant error-corrected framework suffers from a `conservation of unpleasantness'. In general, no matter what error correction technique is employed, there is always one element of a universal gate set that carries a significant resource overhead - either in physical qubits, computational time, or both. Specifically, this is due to the application of non-Clifford gates. A common method for realising these gates for stabiliser codes such as the surface code is a combination of three protocols: state injection, distillation and gate teleportation. These protocols contribute to the resource overhead compared to logical operations such as a CNOT gate and contributes to the qubit resources for any error-corrected quantum algorithm. In this paper, we introduce a very simple protocol to potentially reduce this overhead for non-Clifford gates: Transversal Injection. Transversal injection modifies the initial physical states of all data qubits in a stabiliser code before standard encoding and results in the direct preparation of a large class of single qubit states, including resource states for non-Clifford logic gates. Preliminary results hint at high quality fidelities at larger distances and motivate further research on this technique.

quant-ph↗

arXiv2022

Quantum Fourier Addition, Simplified to Toffoli Addition

Quantum addition circuits are considered being of two types: 1) Toffolli-adder circuits which use only classical reversible gates (CNOT and Toffoli), and 2) QFT-adder circuits based on the quantum Fourier transformation. We present the first systematic translation of the QFT-addition circuit into a Toffoli-based adder. This result shows that QFT-addition has fundamentally the same fault-tolerance cost (e.g. T-count) as the most cost-efficient Toffoli-adder: instead of using approximate decompositions of the gates from the QFT circuit, it is more efficient to merge gates. In order to achieve this, we formulated novel circuit identities for multi-controlled gates and apply the identities algorithmically. The employed techniques can be used to automate quantum circuit optimisation heuristics.

quant-ph↗

arXiv2022

Machine Learning Optimization of Quantum Circuit Layouts

The quantum circuit layout (QCL) problem is to map a quantum circuit such that the constraints of the device are satisfied. We introduce a quantum circuit mapping heuristic, QXX, and its machine learning version, QXX-MLP. The latter infers automatically the optimal QXX parameter values such that the layed out circuit has a reduced depth. In order to speed up circuit compilation, before laying the circuits out, we are using a Gaussian function to estimate the depth of the compiled circuits. This Gaussian also informs the compiler about the circuit region that influences most the resulting circuit's depth. We present empiric evidence for the feasibility of learning the layout method using approximation. QXX and QXX-MLP open the path to feasible large scale QCL methods.

quant-ph↗

arXiv2022

Logical and Physical Reversibility of Conservative Skyrmion Logic

Magnetic skyrmions are nanoscale whirls of magnetism that can be propagated with electrical currents. The repulsion between skyrmions inspires their use for reversible computing based on the elastic billiard ball collisions proposed for conservative logic in 1982. Here we evaluate the logical and physical reversibility of this skyrmion logic paradigm, as well as the limitations that must be addressed before dissipation-free computation can be realized.

cs.ET↗

arXiv2021

Fast quantum circuit simulation using hardware accelerated general purpose libraries

Quantum circuit simulators have a long tradition of exploiting massive hardware parallelism. Most of the times, parallelism has been supported by special purpose libraries tailored specifically for the quantum circuits. Quantum circuit simulators are integral part of quantum software stacks, which are mostly written in Python. Our focus has been on ease of use, implementation and maintainability within the Python ecosystem. We report the performance gains we obtained by using CuPy, a general purpose library (linear algebra) developed specifically for CUDA-based GPUs, to simulate quantum circuits. For supremacy circuits the speedup is around 2x, and for quantum multipliers almost 22x compared to state-of-the-art C++-based simulators.

quant-ph↗

arXiv2021

Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network

Predicting the optimum SWAP depth of a quantum circuit is useful because it informs the compiler about the amount of necessary optimization. Fast prediction methods will prove essential to the compilation of practical quantum circuits. In this paper, we propose that quantum circuits can be modeled as queuing networks, enabling efficient extraction of the parallelism and duration of SWAP circuits. To provide preliminary substantiation of this approach, we compile a quantum multiplier circuit and use a queuing network model to accurately determine the quantum circuit parallelism and duration. Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.

quant-ph↗

arXiv2021

NISQ circuit compilation is the travelling salesman problem on a torus

Noisy, intermediate-scale quantum (NISQ) computers are expected to execute quantum circuits of up to a few hundred qubits. The circuits have to conform to NISQ architectural constraints regarding qubit allocation and the execution of multi-qubit gates. Quantum circuit compilation (QCC) takes a nonconforming circuit and outputs a compatible circuit. Can classical optimisation methods be used for QCC? Compilation is a known combinatorial problem shown to be solvable by two types of operations: 1) qubit allocation, and 2) gate scheduling. We show informally that the two operations form a discrete ring. The search landscape of QCC is a two dimensional discrete torus where vertices represent configurations of how circuit qubits are allocated to NISQ registers. Torus edges are weighted by the cost of scheduling circuit gates. The novelty of our approach uses the fact that a circuit's gate list is circular: compilation can start from any gate as long as all the gates will be processed, and the compiled circuit has the correct gate order. Our work bridges a theoretical and practical gap between classical circuit design automation and the emerging field of quantum circuit optimisation.

quant-ph↗