PaperlayerView exact revision on arXiv

Source-linked AI summary

Shuttle-Exploiting Attacks and Their Defenses in Trapped-Ion Quantum Computers

Abdullah Ash Saki, Rasit Onur Topaloglu, Swaroop Ghosh

arXiv:2108.01054v1quant-ph

TL;DR

Multi-programmed trapped-ion systems expose a shuttle-based security vulnerability because repeated shuttling raises chain energy and degrades gate fidelity. The paper describes attack programs and defenses, reporting fidelity reductions across evaluated trap-capacity and victim-length settings.

  • Problem

    Multi-programming can improve hardware utilization but creates shared-device security risks, and a crosstalk-based attack reported for superconducting qubits does not apply to trapped-ion systems.

    Source: §I · p. 2

  • Method

    The attack has an adversary share a trap with victim qubits and induce repeated shuttles that increase the shared ion-chain’s vibrational energy.

    Source: 4) Shuttle steps · p. 4

  • Results

    2.71X, 5.66X, and 13.51X average fidelity reductions for trap cap 20 at victim lengths 60, 80, and 100; cap 25 gives 4.77X, 15.65X, and 68.13X.

    Source: 1) Systematic attack · p. 9

  • Takeaways & Limitations

    The proposed mitigations are hybrid initial mapping, padding victim programs with dummy qubits, and capping the maximum number of shuttles.

    Source: §VIII · p. 11

Abstract

from arXiv · show

Trapped-ion (TI) quantum bits are a front-runner technology for quantum computing. TI systems with multiple interconnected traps can overcome the hardware connectivity issue inherent in superconducting qubits and can solve practical problems at scale. With a sufficient number of qubits on the horizon, the multi-programming model for Quantum Computers (QC) has been proposed where multiple users share the same QC for their computing. Multi-programming is enticing for quantum cloud providers as it can maximize device utilization, throughput, and profit for clouds. Users can also benefit from the short wait queue. However, shared access to quantum computers can create new security issues. This paper presents one such vulnerability in shared TI systems that require shuttle operations for communication among traps. Repeated shuttle operations increase quantum bit energy and degrade the reliability of computations (fidelity). We show adversarial program design approaches requiring numerous shuttles. We propose a random and systematic methodology for adversary program generation. Our analysis shows shuttle-exploiting attacks can substantially degrade the fidelities of victim programs by 2X to 63X. Finally, we present several countermeasures such as adopting a hybrid initial mapping policy, padding victim programs with dummy qubits, and capping maximum shuttles.

I. INTRODUCTION

Multi-trap trapped-ion systems offer a route to scalable connectivity, while multi-programming can improve hardware utilization but introduces shared-device security risks. This paper identifies repeated shuttling as a trapped-ion attack vector and outlines attack-generation methods and defenses.

I. INTRODUCTION

  • Interconnected traps support trapped-ion scalability because confining many ions in one trap creates control and gate-implementation problems.The passage describes QCCD as a proposed technology for scalable, modular trapped-ion systems.Source: §I · p. 1
  • Multi-programming schedules users’ programs together to improve resource utilization, throughput, and cloud profit, but shared execution creates security risks.A crosstalk-induced attack reported for superconducting qubits does not apply to trapped-ion systems because crosstalk is negligible.Source: §I · p. 2
  • Repeated shuttles raise shared-chain energy and degrade gate fidelity, letting an adversary program affect victim qubits sharing its trap.The attack requires program design that overcomes architectural policies that otherwise curb shuttling.Source: §I · p. 2
  • The paper proposes systematic and random malicious-program generation; systematic design uses architectural-policy knowledge, while random design needs no prior knowledge but is less potent.The systematic and random approaches correspond to white-box and black-box attack settings, respectively.Source: §I · p. 2
  • The analysis examines how trap capacity and victim size affect shuttle counts and victim-fidelity reduction.The paper also proposes hybrid initial mapping, dummy-qubit padding, and a maximum-shuttle limit as countermeasures.Source: §I · p. 2

II. BASICS

The basics introduce qubits and quantum gates, explain how fidelity relates to gate errors, and describe trapped-ion traps and shuttling.

1) Qubits:

  • Qubits encode data in internal states and can occupy a superposition of |0⟩ and |1⟩ until measurement returns a classical bit.Measurement returns 0 or 1 with probabilities determined by the state amplitudes.Source: 1) Qubits · p. 3

2) Quantum gates:

  • Quantum gates manipulate qubit information using pulses, and a quantum program is a sequence of gates, including two-qubit MS gates.Physically realized gates include one- and two-qubit gates.Source: 2) Quantum gates · p. 3

3) Gate fidelity:

  • Gate fidelity is the complement of error rate; lower fidelity introduces more output errors.The passage illustrates this with 100 incorrect outcomes out of 10,000 repeated X gates.Source: 3) Gate fidelity · p. 3

1) Trap details:

  • In Fig. 3, two-qubit gates execute directly when both ions are in one trap, while ions in separate traps require shuttling.The paper focuses on two-qubit gates because they have lower fidelity and require shuttle operations.Source: Figure 3 · p. 3
  • Trapped-ion systems hold ions in chains inside connected traps; trap capacity and communication capacity bound how many ions each trap can hold.The excess capacity is trap capacity plus communication capacity minus the ions in the trap.Source: 1) Trap details · p. 3

2) Gate details:

Two-qubit MS gates require ions to share a trap; shuttle operations move ions between traps, while mapping and shuttle-direction policies determine allocation and movement.

2) Gate details:

  • Two-qubit gates cannot operate across traps, and the paper states they typically have an order of magnitude lower fidelity than one-qubit gates.The gate-fidelity model depends on gate time and chain motional mode; increasing either degrades fidelity.Source: 2) Gate details · p. 4

3) Need of shuttle operation::

  • A gate between ions in separate traps requires shuttling one ion into the other ion’s trap before execution.The sample contrasts a same-trap gate executed in place with a cross-trap gate that needs a shuttle.Source: 3) Need of shuttle operation · p. 4

4) Shuttle steps:

  • Shuttling a1 from T0 to T1 involves swapping it toward the path, splitting it from its chain, moving it, and merging it into the destination chain.The merge increases that chain’s vibrational energy, lowering fidelity for subsequent gates on ions in the chain.Source: 4) Shuttle steps · p. 4

C. Initial mapping policy

  • The greedy initial mapping policy allocates qubits in descending order of two-qubit-gate edge weights, prioritizing the most frequent pairs.The sample’s edge weights determine the order in which qubits are allocated across traps.Source: C. Initial mapping policy · p. 4

D. Shuttle direction policy

  • When traps have equal excess capacity, the shuttle-direction policy moves the first ion of the gate from T0 to T1.The policy otherwise selects a direction based on the traps’ relative excess capacities.Source: D. Shuttle direction policy · p. 4

E. Multi-programming

The attack targets shared trapped-ion programs by inducing repeated shuttles, under stated multi-programming and adversary-knowledge assumptions.

E. Multi-programming

  • The multi-programming mapping allocates two programs from opposite ends of different traps, keeping their qubits from being mixed.Within each program, qubits are allocated using the greedy mapping policy.Source: E. Multi-programming · p. 4

A. Attack model

  • The model assumes concurrent programs, an adversary spanning two traps and sharing one with the victim, and access to compiled programs and device specifications.Knowledge of mapping and shuttle-direction policies is also assumed, though the paper says this assumption can be relaxed.Source: A. Attack model · p. 4
  • The adversary designs a program to cause repeated shuttles, increasing shared-chain vibrational energy and degrading gate fidelities; the adversary’s own fidelity also suffers.The stated objective is to affect the victim program.Source: A. Attack model · p. 5
  • Systematic generation uses prior architectural knowledge and guarantees a linear shuttle increase with program length; random generation needs no prior knowledge but lacks that guarantee.Both methods can degrade victim performance.Source: A. Attack model · p. 5

B. Simulation setup

The section describes the simulation setup and two attack-program generation approaches: randomized black-box search and systematic construction, including pruning and program blocks.

B. Simulation setup

  • Simulations use a two-trap linear device, trap capacities of 15, 20, or 25 ions, and two additional communication spaces per trap.The adversary spans two traps; victim sizes vary over the remaining shared-trap capacity, and program fidelity is computed as the product of two-qubit gate fidelities.Source: B. Simulation setup · p. 5

IV. RANDOMIZED MALICIOUS PROGRAM

  • Randomized generation treats the compiler as a black box: the adversary submits programs, inspects compiled shuttle counts, and selects a high-shuttle candidate.The described search generates 1,000 random circuits, compiles them against pseudo-victims of sizes 2–12, and selects the circuit with the highest average shuttle count across sizes.Source: §IV · p. 5, A. General methodologies · p. 5

B. Pruning the random circuit

  • Pruning tests gates one by one and permanently removes a gate when its removal does not reduce the shuttle count.On average, pruning removed about 48 gates from a 153-gate program.Source: B. Pruning the random circuit · p. 5

A. Basic idea

  • The systematic method tracks ion locations using the initial mapping policy, shuttle-direction policy, and victim size, then adds gates between ions in different traps.This construction requires knowledge of the three ingredients to track ion locations.Source: A. Basic idea · p. 5
  • The systematic malicious program comprises a shuttle controller (SC), bridging gate, and initial mapping controller (IMC), generated in IMC-then-SC-then-bridge order and stitched together.Source: A. Basic idea · p. 5

B. Initial mapping controller

The initial mapping controller uses deliberately increased gate frequencies to influence the compiler’s greedy initial mapping. This places the adversary’s ions across the traps in a known arrangement.

B. Initial mapping controller

  • The method increases selected edge weights by adding gates between chosen ions, prioritizing those ions during greedy allocation.The example adds each selected gate twice to give its edge weight 2.Source: B. Initial mapping controller · p. 6
  • Duplicating gates in the IMC gives them higher edge weights, so the greedy compiler allocates those ions first.The remaining three adversary qubits are allocated to the second trap by default.Source: B. Initial mapping controller · p. 6
  • With trap capacity 15 and an 18-qubit adversary program, the initial mapping leaves 15 adversary ions in each trap and two excess-capacity slots per trap.The second trap also contains the 12-qubit victim in the example.Source: B. Initial mapping controller · p. 6

C. Shuttle controller

The shuttle controller builds a gate block that repeatedly selects ions in different traps, tracks their movement, and updates trap states after each shuttle.

C. Shuttle controller

  • The controller tracks ion locations after each gate and selects ions from different traps for the next gate.This uses the shuttle-direction policy to update locations and trap states.Source: C. Shuttle controller · pp. 6–7
  • For each gate, it selects an ion, finds the opposite trap, and chooses a pair whose gate is not already in the program.The example selects ion 14, then ion 15 from the opposite trap for a new gate.Source: C. Shuttle controller · pp. 6–7
  • A selected cross-trap gate requires a shuttle, after which the controller updates trap states and edge weights before continuing until the target block length.The example’s first gate moves ion 14 according to the traps’ relative excess capacities.Source: C. Shuttle controller · p. 7

1) The bridging gate:

The systematic attack links its shuttle-controller and initial-mapping blocks with one bridging gate, then combines them into a malicious program. Its performance depends on victim-size assumptions, which the method selects using shuttle-count consistency.

1) The bridging gate:

E. Discussions on the systematic method

2) Necessity of victim size information:

  • The assumed victim size affects whether each shuttle-controller gate forces a shuttle; an inaccurate assumption can leave some gates without one.The example with a 12-qubit assumption and an actual victim size of 5 shows a later gate that does not require a shuttle.Source: 2) Necessity of victim size information · pp. 7–8
  • At trap capacity 15, assuming a victim size of 8 gives the highest ICV; the best assumptions are 13 at capacity 20 and 16 at capacity 25.The method sweeps assumptions and selects the value with the highest ICV, a normalized mean-to-standard-deviation ratio.Source: Figure 8 · p. 8, Table I · p. 8, 2) Necessity of victim size information · p. 8

3) Availability of architectural policies:

The systematic method relies on knowledge of initial-mapping and shuttle-direction policies, but the authors describe alternatives when those policies are unavailable. Its shuttle performance varies with victim size and program length.

3) Availability of architectural policies:

  • The systematic method relies on knowing initial-mapping and shuttle-direction policies; if those are protected, the authors suggest trying multiple policies or using the random attack.The random attack does not require prior policy information.Source: 3) Availability of architectural… · p. 8

1) Systematic attack:

  • With an assumed victim size of 2, the attack achieves 80 moves when the actual victim size is also 2; counts typically drop for other actual sizes.The figure covers actual victim sizes from 2 to 12 at trap capacity 15.Source: 1) Systematic attack · p. 8
  • Fidelity reduction is positively correlated with victim-program length when the adversary assumes a victim size of 8.The plotted trend is reported for varying victim lengths.Source: Figure 9 · p. 9
  • At trap capacity 15, assuming a victim size of 8 gives the highest ICV across actual victim sizes; the best assumptions are 13 and 16 for capacities 20 and 25.The shuttle count is highest when the assumed victim size matches the actual size.Source: 1) Systematic attack · p. 9

2) Random attack:

The paper compares systematic and random shuttle-exploiting attacks, their effects on victim fidelity, and how attack potency depends on victim length, trap capacity, and initial mapping. It also weighs the two attack-generation approaches against the resources and architectural knowledge available to an adversary.

2) Random attack:

  • Victim size 6 provides the best ICV results in the random-attack victim-size sweep and is selected for subsequent attacks.The passage reports the selection but does not specify the ICV values.Source: 2) Random attack · p. 9

1) Systematic attack:

  • Average fidelity reductions under attack are 2.63X, 5.40X, and 13.11X for victim lengths 60, 80, and 100, respectively, with an 80-gate SC block, 15-ion trap cap, and assumed 8-qubit victim.Source: 1) Systematic attack · p. 9
  • For trap cap 20, average fidelity reductions are 2.71X, 5.66X, and 13.51X for victim lengths 60, 80, and 100; for trap cap 25, they are 4.77X, 15.65X, and 68.13X.The paper attributes worse fidelity at larger capacities, especially for larger victims, to scaling factor A increasing with the number of ions and amplifying the motional-mode effect.Source: 1) Systematic attack · p. 9, Table III · p. 10

2) Random attack:

  • 2.22X, 4.0X, and 8.94X average fidelity reductions are reported for the random attack at trap cap 15, and are lower than for systematic attack.The paper omits random-attack values for trap caps 20 and 25, noting reductions would be higher at those capacities.Source: 2) Random attack · p. 9, Figure 11 · p. 10

C. Choice between systematic and random attack programs

  • Systematic generation yields a strong attack in one try when architectural policies are known; random generation suits adversaries without that knowledge or with resources to run many programs.Running programs on the cloud costs money, and the paper says random attack programs have lower potency than systematic ones.Source: C. Choice between systematic and… · p. 9

A. Random initial mapping

  • Random initial mapping reduced shuttles from 80 to an average of approximately 27 (σ = 9.45), a 2.96X drop across 1000 runs.The experiment uses an 18-qubit adversary program designed for a 12-qubit victim; random mapping disrupts attacks that depend on consistent allocation and known trap states.Source: A. Random initial mapping · pp. 9–10

1) Trade-off of random initial mapping:

The paper proposes mapping, padding, and shuttle-cap defenses, while noting costs to legitimate users and cloud throughput. Its conclusion identifies shuttle operations as an attack mode in multi-programmed trapped-ion systems.

1) Trade-off of random initial mapping:

  • Random initial mapping increases shuttles in NISQ benchmarks by up to 6X, despite weakening attacks, and can therefore penalize legitimate users.The evaluated circuits include QFT, QAOA, a supremacy circuit, and a quantum adder; random-policy means use 1000 random allocations.Source: 1) Trade-off of random initial mapping · p. 10, Table IV · p. 10

2) Hybrid approach:

  • A hybrid compiler can compare random and intelligent initial mappings and execute whichever version requires fewer shuttles.The described intelligent policy includes greedy mapping.Source: 2) Hybrid approach · p. 10

B. Dummy pad qubits in the victim program

  • Padding unused trap capacity with dummy qubits can prevent adversary qubits from sharing the victim's trap and causing shuttle-induced fidelity degradation.Virtual-Z gates on dummy qubits ensure the compiler considers them during allocation; the paper says these software gates have perfect fidelity and require no physical operation.Source: B. Dummy pad qubits in the victim… · p. 10
  • Padding trades security for cost: under a linear model, five dummy qubits raise a 10-qubit program's cost from 10 to 15 units, or 1.5X.The paper notes cost could scale exponentially with qubit count and says padding is more cost-effective when few dummy qubits are needed.Source: B. Dummy pad qubits in the victim… · p. 10

C. Capping maximum number of allowed shuttles

  • Capping allowed shuttles lets the cloud schedule programs exceeding the limit separately, but switching to single-programming can reduce throughput.The paper suggests charging extra for high-shuttle programs to cover the utilization loss; its example uses 30 resources for an 18-qubit adversary program.Source: C. Capping maximum number of allowed… · pp. 10–11

VIII. CONCLUSION

  • The paper's conclusion frames shuttle operations as an attack mode in multi-programmed trapped-ion systems and identifies hybrid mapping, dummy-qubit padding, and shuttle caps as mitigations.These measures correspond to compiler, victim-program, and cloud-provider interventions described in the section.Source: §VIII · p. 11
Loading 2108.01054v1…