2025-11-02 - AQSE - Qiskit Fall Fest - IIT-Guwahati.pdf

2025-11-02 - AQSE - Qiskit Fall Fest - IIT-Guwahati.pdf

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  Aritra Sarkar 2025.11.02 Keynote @Qiskit Fall Fest - IIT Guwahati Automating Quantum Computing Software  Application  System
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  B.Tech Avionics (specialisation: SpaceRobotics) Indian Institute of Space Science and Technology 2009-2013 Scientist Satellite Onboard Control and Digitals Subsystem Indian Space Research Organisation 2013-2016 Postdoctoral Researcher Quantum Machine Learning group Quantum Computing division, QuTech 2022-2024 M.Sc. Computer Engineering cum laude + Ph.D. Quantum Computer Architecture group Department of Quantum & Computer Engineering 2016-2018 + 2018-2022 QCA QIT XAI Senior Researcher Quantum Technology Division Fujitsu Research India Private Limited 2024-present
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  a Device thatuses the Laws of Quantum Information for Computational Advantage 1 2 + = classical computers, small quantum computers 3 2 + = quantum computational model 3 1 + = accelerators (e.g., GPU) 1 2 3
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  Computer Science Quantum Physics ❑ from understandingto control ❑ from passive to active ❑ sensors, communication, simulation, computation ❑ from science to technology ❑ new ways of computing ❑ reversible ❑ resource complexity ❑ natural quantum simulators Computer Engineering ❑ need for computing ever increasing ❑ ₹ → faster computers → smaller transistors → quantum effects → accelerators and ASICs → ₹
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  “properties” of bothclassical waves (when we are not looking) and classical particles (when we look) Superposition – Interference – Measurement
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  Superposition – Entanglement– Magic – Interference – Measurement INTERFERENCE sour savory bitter sweet taste food if: then: mix them up food wt. const. sour savory bitter sweet object properties SUPERPOSITION ENTANGLEMENT MEASUREMENT
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  ● Representing quantuminformation: • Physically: energy levels, polarization, spins • Conceptually: 2-level qubits, qudits ● Mathematical model: • Linear combination of states • Weighted by a complex number (amplitude) • Modulus of amplitude = probability of observing • The probabilities add up to 1 • Each trial give a different outcome (need the bias) • Each qubit doubles the number of states ● Why such a weird model? • because... that agrees with the experiments! 0 (and/or) 1 2n states 1 state when observed q = z0 + z1 q = 1 q = 1 p(0)=|z0| p(1)=|z1| Kurzgesagt – In a Nutshell
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  Gate-based QC Universal TM Universal QTM … algorithm a implementation C2a CXa= QXa*[QX:CX]n physical process problemsize n Adiabatic QC Cellular Automata … λ Calculus implementation C1a C2a= C1a*n[C1:C2] implementation Q1a implementation Q2a Q2a= Q1a*n[Q1:Q2] quantum
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  1. Same Turingdegree in arithmetic hierarchy as CC, no hypercomputation 1. Must beat SotA HPC QC sims (e.g., tensor networks-based simulators) 2. QC > CC only in resources exp/poly 1. BQPBPP few; BQPBPP + Practical use cases: fewer 2. Advantage in other resources, e.g., space complexity (memory efficiency), generalization, representation capacity 3. Hard to infer quantum advantage from code structure (e.g., embarrassingly parallel => GPU) 2. Many industrial problems are NP-hard 1. NP-complete is most likely outside BQP, thus, no exponential advantage 2. NP problems can be solved with quantum search; poly speedup over CC if no better heuristics known 3. QC models are poly equivalent, CC models are poly equivalent 1. Poly advantage w.r.t. CC in 1 QC model might get negated in another QC model 2. Poly advantage w.r.t. CC in 1 QC model might be dequantized in another CC model 4. Asymptotic complexity might cross over to quantum advantage at impractical problem size https://arxiv.org/abs/2212.00619 Automated Quantum Software Engineering: why? what? how?
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  NISQ FTQC QEC Classical Simulation Limit number of qubits error rate NISQ: Noisy Intermediate-Scale Quantum  FTQC: Fault Tolerant Quantum Computing
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  Software 1.0 -explicit instructions to the computer written by a programmer Software 2.0 - specify desirable behavior of a program and search Software 3.0 - prompt-based, vibe coding, agentic, etc.
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  How are quantumalgorithm designed? Approach 1: quantum complexity idealists • Given: BPP ⊂ BQP; set of universal quantum gates • Find: new quantum algorithms for specific mathematical properties • E.g.: a super-polynomial speedup in determining the zeta function of a genus curve over a finite field • Focus: asymptotic speedup w.r.t. best classical approach based on ideal qubits, gates, connectivity, etc. • Automate: quantum information theory
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  How are quantumalgorithm designed? Approach 2: quantum transformation wizards • Given: an industrial computational use case • Find: tweak an existing quantum algorithm • E.g.: a pipeline for satellite image processing using one quantum convolution layer on a neural network architecture • Focus: PoC quantum kernel (maybe QC simulation) embedded within an existing application framework • Automate: quantize problem formulation (qubo/sat to parametric circuits), data embedding and training (classical optimizer in PQC)
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  How are quantumalgorithm designed? Approach 3: quantum advantage torchbearers • Given: constraints of a specific QPU • Find: demonstration of a PoC for a promising use case • E.g.: the protein-folding problem on a tetrahedral lattice using a hybrid classical-quantum algorithm with pulse-level optimized control on an IBM Eagle 127-qubit QPU • Focus: extracting as much computation power as possible on NISQ hardware using hardware-software co- design • Automate: quantum control (pulse-shaping), low-level compilation (circuit optimization, scheduling, mapping,…)
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  1.5yr 1.5yr 1.5yr
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  Quantum Computing Stack Quantum Micro- architecture
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  Kurzgesagt – Ina Nutshell
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  ~2018 A Microarchitecture fora Superconducting Quantum Processor 10.1109/MM.2018.032271060
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  mathematical abstractions of quantum algorithms QISA Pulses physicalmanipulation of quantum hardware QASM Hamiltonians Super-operators Decoherence time Unitary gates Noise models Complexity classes compiler microarchitecture analog control Heisenberg interface classical finite descriptions
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  Quantum-accelerated Genome SequenceReconstruction
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  Quantum Universal ReinforcementLearning
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  Yet Another QuantumQuantizer (YAQQ) Are all QC alike? Theorist: Yes! Church-Turing-Deutsch principle: • Universal quantum computation set can simulate any physical dynamics • QTM later generalized to the circuit model and k-local gate sets ({H,T,CX}, {D(θ)}, {Toffoli,H}) Solovay-Kitaev theorem: • Iterated shrinking lemma: find better approx. of U by recursively inc. decomposed circuit length • Universal QGS approximate the Hilbert space with 𝑂(𝑙𝑛3.97 Τ 1 𝜀) gates in 𝑂(𝑙𝑛2.71 Τ 1 𝜀) compiler time • Can be generalized to n-qubits/𝑆𝑈(𝑑) at exp. scaling Experimentalist: No! DiVincenzo criteria: • Scalable (bounds circuit max. width), well- characterized qubits • Long decoherence time (bounds circuit max. depth) • Universal native gate set • Initialization to fiducial state • Measure individual qubits Qubit plane architecture: • Connectivity bounds information interaction rate between qubits. Needs additional gates for routing (taking some share of max. depth) • Not all qubit noise are uniform. Noise drift.
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  Cryogenic-CMOS for QuantumComputing Energy-efficient Quantum Instruction Set Architecture (EQISA) ≡ Quantum Circuit Description Complexity ≡ bits required to erase/uncompute a qubit to a known state ≡ least no. of control bits to prepare the current state from a known state ≡ coding and compressing scheme for quantum circuits • e.g., CISC for low-power embedded systems • trade off 4K-RT compressed ins. bw with ASIC/FPGA proc. @ 2.46pJ/b, 40Gb/s Coding + Concept Discovery Ver-0: Binary coded Native Gate Set Ver-1: Huffman coded Native Gate Set Ver-2: Huffman coded SK Basis Approx. Ver-3: Huffman coded SK Basis Approx. + cutoff
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  Energy-efficient Quantum OptimalControl (EO-GRAPE & EO-DRLPE) Quantum Speed Limits for U • Discrete: Σ (# gates * gate-time) • Continuous: Energy ≡ Geodesics on Riemannian manifold ≡ Lagrangian mechanics Trade off fidelity and energy for pulse-level gate synthesis using Energy-optimized Gradient Ascent Pulse Engineering (EO-GRAPE) 8% ↓ energy 1% ↓ fidelity (< Pthresh) U = H wfe = [0.8, 0.2]
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  Quantum Information TheoreticCompilation
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  Generative AI forQuantum Computer Engineering
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  eXplainable Quantum MachineLearning
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  …others ❑ A resource-efficientvariational quantum algorithm for mRNA codon optimization ❑ Efficient parameterised compilation for hybrid quantum programming ❑ Efficient decomposition of unitary matrices in quantum circuit compilers ❑ LEGO_HQEC: A Software Tool for Analyzing Holographic Quantum Codes ❑ Near-term spin-qubit architecture design via multipartite maximally entangled states ❑ ArtA: Automating Design Space Exploration of Spin-Qubit Architectures ❑ CutQAS: Topology-aware quantum circuit cutting via reinforcement learning ❑ A Scalable Quantum Gate‐Based Implementation for Causal Hypothesis Testing ❑ Visualizing quantum circuit probability: estimating quantum state complexity for quantum program synthesis + current research at Fujitsu
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  ✓ The bestmodel of computing allowed by current laws of physics. ✓ Business advantage likely decades away. Scientific use cases nearer. ✓ Interdisciplinary expertise required in both theory and engineering. ✓ Rapidly evolving, need to stay up to date. ✓ Get started with quantum programming via hackathons and summer schools. ✓ AI and QC synergy will be crucial for both fields. ✓ Beware of the hype! But don’t be ignorant of the enormous potential. Takeaways